Athero-Express Biobank Study – IL6 and MCP1 plaque levels.
An ‘Athero-Express Biobank Study’ project
Sander W. van der Laan, PhD | @swvanderlaan; Marios Georgakis; Rainer Malik; Martin Dichgans
2020-03-05
Preparation
Clean the environment.
Set locations, and the working directory.
A package-installation function.
Load those packages.
We will create a datestamp and define the Utrecht Science Park Colour Scheme.
# Function to grep data from glm()/lm()
GLM.CON <- function(fit, DATASET, x_name, y, verbose=c(TRUE,FALSE)){
cat("Analyzing in dataset '", DATASET ,"' the association of '", x_name ,"' with '", y ,"' .\n")
if (nrow(summary(fit)$coefficients) == 1) {
output = c(DATASET, x_name, y, rep(NA,8))
cat("Model not fitted; probably singular.\n")
}else {
cat("Collecting data.\n\n")
effectsize = summary(fit)$coefficients[2,1]
SE = summary(fit)$coefficients[2,2]
OReffect = exp(summary(fit)$coefficients[2,1])
CI_low = exp(effectsize - 1.96 * SE)
CI_up = exp(effectsize + 1.96 * SE)
tvalue = summary(fit)$coefficients[2,3]
pvalue = summary(fit)$coefficients[2,4]
R = summary(fit)$r.squared
R.adj = summary(fit)$adj.r.squared
sample_size = nrow(model.frame(fit))
AE_N = AEDB.CEA.samplesize
Perc_Miss = 100 - ((sample_size * 100)/AE_N)
output = c(DATASET, x_name, y, effectsize, SE, OReffect, CI_low, CI_up, tvalue, pvalue, R, R.adj, AE_N, sample_size, Perc_Miss)
if (verbose == TRUE) {
cat("We have collected the following and summarize it in an object:\n")
cat("Dataset...................:", DATASET, "\n")
cat("Score/Exposure/biomarker..:", x_name, "\n")
cat("Trait/outcome.............:", y, "\n")
cat("Effect size...............:", round(effectsize, 6), "\n")
cat("Standard error............:", round(SE, 6), "\n")
cat("Odds ratio (effect size)..:", round(OReffect, 3), "\n")
cat("Lower 95% CI..............:", round(CI_low, 3), "\n")
cat("Upper 95% CI..............:", round(CI_up, 3), "\n")
cat("T-value...................:", round(tvalue, 6), "\n")
cat("P-value...................:", signif(pvalue, 8), "\n")
cat("R^2.......................:", round(R, 6), "\n")
cat("Adjusted r^2..............:", round(R.adj, 6), "\n")
cat("Sample size of AE DB......:", AE_N, "\n")
cat("Sample size of model......:", sample_size, "\n")
cat("Missing data %............:", round(Perc_Miss, 6), "\n")
} else {
cat("Collecting data in summary object.\n")
}
}
return(output)
print(output)
}
GLM.BIN <- function(fit, DATASET, x_name, y, verbose=c(TRUE,FALSE)){
cat("Analyzing in dataset '", DATASET ,"' the association of '", x_name ,"' with '", y ,"' ...\n")
if (nrow(summary(fit)$coefficients) == 1) {
output = c(DATASET, x_name, y, rep(NA,9))
cat("Model not fitted; probably singular.\n")
}else {
cat("Collecting data...\n")
effectsize = summary(fit)$coefficients[2,1]
SE = summary(fit)$coefficients[2,2]
OReffect = exp(summary(fit)$coefficients[2,1])
CI_low = exp(effectsize - 1.96 * SE)
CI_up = exp(effectsize + 1.96 * SE)
zvalue = summary(fit)$coefficients[2,3]
pvalue = summary(fit)$coefficients[2,4]
dev <- fit$deviance
nullDev <- fit$null.deviance
modelN <- length(fit$fitted.values)
R.l <- 1 - dev / nullDev
R.cs <- 1 - exp(-(nullDev - dev) / modelN)
R.n <- R.cs / (1 - (exp(-nullDev/modelN)))
sample_size = nrow(model.frame(fit))
AE_N = AEDB.CEA.samplesize
Perc_Miss = 100 - ((sample_size * 100)/AE_N)
output = c(DATASET, x_name, y, effectsize, SE, OReffect, CI_low, CI_up, zvalue, pvalue, R.l, R.cs, R.n, AE_N, sample_size, Perc_Miss)
if (verbose == TRUE) {
cat("We have collected the following and summarize it in an object:\n")
cat("Dataset...................:", DATASET, "\n")
cat("Score/Exposure/biomarker..:", x_name, "\n")
cat("Trait/outcome.............:", y, "\n")
cat("Effect size...............:", round(effectsize, 6), "\n")
cat("Standard error............:", round(SE, 6), "\n")
cat("Odds ratio (effect size)..:", round(OReffect, 3), "\n")
cat("Lower 95% CI..............:", round(CI_low, 3), "\n")
cat("Upper 95% CI..............:", round(CI_up, 3), "\n")
cat("Z-value...................:", round(zvalue, 6), "\n")
cat("P-value...................:", signif(pvalue, 8), "\n")
cat("Hosmer and Lemeshow r^2...:", round(R.l, 6), "\n")
cat("Cox and Snell r^2.........:", round(R.cs, 6), "\n")
cat("Nagelkerke's pseudo r^2...:", round(R.n, 6), "\n")
cat("Sample size of AE DB......:", AE_N, "\n")
cat("Sample size of model......:", sample_size, "\n")
cat("Missing data %............:", round(Perc_Miss, 6), "\n")
} else {
cat("Collecting data in summary object.\n")
}
}
return(output)
print(output)
}Background
Using a Mendelian Randomization approach, we recently examined associations between the circulating levels of 41 cytokines and growth factors and the risk of stroke in the MEGASTROKE GWAS dataset (67,000 stroke cases and 450,000 controls) and found Monocyte chemoattractant protein-1 (MCP-1) as the cytokine showing the strongest association with stroke, particularly large artery and cardioembolic stroke (Georgakis et al., 2019a). Genetically elevated MCP-1 levels were also associated with a higher risk of coronary artery disease and myocardial infarction (Georgakis et al., 2019a). Further, in a meta-analysis of 6 observational population-based of longitudinal cohort studies we recently showed that baseline levels of MCP-1 were associated with a higher risk of ischemic stroke over follow-up (Georgakis et al., 2019b). While these data suggest a central role of MCP-1 in the pathogenesis of atherosclerosis, it remains unknown if MCP-1 levels in the blood really reflect MCP-1 activity. MCP-1 is expressed in the atherosclerotic plaque and attracts monocytes in the subendothelial space (Nelken et al., 1991; Papadopoulou et al., 2008; Takeya et al., 1993; Wilcox et al., 1994). Thus, MCP-1 levels in the plaque might more strongly reflect MCP-1 signaling. However, it remains unknown if MCP-1 plaque levels associate with plaque vulnerability or risk of cardiovascular events.
Objectives
Against this background we now aim to make use of the data from Athero-Express Biobank Study to explore the associations of MCP-1 protein levels in the atherosclerotic plaques from patients undergoing carotid endarterectomy with phenotypes of plaque vulnerability and secondary vascular events over a follow-up of three years.
Methods
Blood
- IL6: Interleukin 6. Entrez Gene: 3569. Bender MedSystems; cat.nr.: BMS810FF. Recalculated FACS. [pg/mL]
- MCP1: Monocyte chemotactic protein 1, MCP-1 (Chemokine (C-C motif) ligand 2, CCL2). Entrez Gene: 6347. Measured at the WKZ. Recalculated Luminex. [pg/mL]
Plaque
- IL6: Interleuking 6 (IL6; Entrez Gene: 3569) concentration in plaque [pg/ug], measured by Luminex at the WKZ.
- MCP1: Monocyte chemotactic protein 1 (a.k.a. CCL2; Entrez Gene: 6347) concentration in plaque [pg/ug], measured by Luminex at the WKZ.
- IL6R: Interleuking 6 receptor (IL6R; Entrez Gene: 3570) concentration in plaque [pg/ug], measured by Luminex at the WKZ.
Loading data
Clinical data
Loading Athero-Express clinical data.
require(haven)
AEDB <- haven::read_sav(paste0(AEDB_loc, "/2019-3NEW_AtheroExpressDatabase_ScientificAE_02072019_IC_added.sav"))
head(AEDB)
require(openxlsx)Loading required package: openxlsxAEDB_ProtConc <- openxlsx::read.xlsx(paste0(AEDB_loc, "/_Vriezer/20190919_Freezers_preTCBio.xlsx"),
sheet = "ProteinConc.",
skipEmptyCols = TRUE)
AEDB_Blood <- openxlsx::read.xlsx(paste0(AEDB_loc, "/_Vriezer/20190919_Freezers_preTCBio.xlsx"),
sheet = "Blood",
skipEmptyCols = TRUE)
AEDB_Plaque <- openxlsx::read.xlsx(paste0(AEDB_loc, "/_Vriezer/20190919_Freezers_preTCBio.xlsx"),
sheet = "Plaque",
skipEmptyCols = TRUE)
head(AEDB_ProtConc)head(AEDB_Blood)head(AEDB_Plaque)NA
NAFixing and creating variables
We need to be very strict in defining symptoms. Therefore we will fix a new variable that groups symptoms at inclusion.
Coding of symptoms is as follows:
- missing -999
- Asymptomatic 0
- TIA 1
- minor stroke 2
- Major stroke 3
- Amaurosis fugax 4
- Four vessel disease 5
- Vertebrobasilary TIA 7
- Retinal infarction 8
- Symptomatic, but aspecific symtoms 9
- Contralateral symptomatic occlusion 10
- retinal infarction 11
- armclaudication due to occlusion subclavian artery, CEA needed for bypass 12
- retinal infarction + TIAs 13
- Ocular ischemic syndrome 14
- ischemisch glaucoom 15
- subclavian steal syndrome 16
- TGA 17
We will group as follows in Symptoms.5G:
- Asymptomatic > 0
- TIA > 1, 7, 13
- Stroke > 2, 3
- Ocular > 4, 14, 15
- Retinal infarction > 8, 11
- Other > 5, 9, 10, 12, 16, 17
We will also group as follows in AsymptSympt:
- Asymptomatic > 0
- TIA > 1, 7, 13 + Stroke > 2, 3
- Ocular > 4, 14, 15 + Retinal infarction > 8, 11 + Other > 5, 9, 10, 12, 16, 17
# Fix symptoms
attach(AEDB)
# Symptoms.5G
AEDB[,"Symptoms.5G"] <- NA
AEDB$Symptoms.5G[sympt == 0] <- "Asymptomatic"
AEDB$Symptoms.5G[sympt == 1 | sympt == 7 | sympt == 13] <- "TIA"
AEDB$Symptoms.5G[sympt == 2 | sympt == 3] <- "Stroke"
AEDB$Symptoms.5G[sympt == 4 | sympt == 14 | sympt == 15 ] <- "Ocular"
AEDB$Symptoms.5G[sympt == 8 | sympt == 11] <- "Retinal infarction"
AEDB$Symptoms.5G[sympt == 5 | sympt == 9 | sympt == 10 | sympt == 12 | sympt == 16 | sympt == 17] <- "Other"
# AsymptSympt
AEDB[,"AsymptSympt"] <- NA
AEDB$AsymptSympt[sympt == 0] <- "Asymptomatic"
AEDB$AsymptSympt[sympt == 1 | sympt == 7 | sympt == 13 | sympt == 2 | sympt == 3] <- "Symptomatic"
AEDB$AsymptSympt[sympt == 4 | sympt == 14 | sympt == 15 | sympt == 8 | sympt == 11 | sympt == 5 | sympt == 9 | sympt == 10 | sympt == 12 | sympt == 16 | sympt == 17] <- "Ocular and others"
detach(AEDB)
AEDB.temp <- subset(AEDB, select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "sympt", "Symptoms.5G", "AsymptSympt"))
require(labelled)Loading required package: labelledAEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
table(AEDB.temp$Symptoms.5G, AEDB.temp$AsymptSympt)
Asymptomatic Ocular and others Symptomatic
Asymptomatic 311 0 0
Ocular 0 408 0
Other 0 118 0
Retinal infarction 0 42 0
Stroke 0 0 720
TIA 0 0 1030rm(AEDB.temp)We will also fix the plaquephenotypes variable.
Coding of symptoms is as follows:
- missing -999
- not relevant -888
- fibrous 1
- fibroatheromatous 2
- atheromatous 3
# Fix plaquephenotypes
attach(AEDB)
AEDB[,"OverallPlaquePhenotype"] <- NA
AEDB$OverallPlaquePhenotype[plaquephenotype == -999] <- NA
AEDB$OverallPlaquePhenotype[plaquephenotype == -999] <- NA
AEDB$OverallPlaquePhenotype[plaquephenotype == 1] <- "fibrous"
AEDB$OverallPlaquePhenotype[plaquephenotype == 2] <- "fibroatheromatous"
AEDB$OverallPlaquePhenotype[plaquephenotype == 3] <- "atheromatous"
detach(AEDB)
AEDB.temp <- subset(AEDB, select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "plaquephenotype", "OverallPlaquePhenotype"))
require(labelled)
AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
rm(AEDB.temp)
We will also fix the diabetes status variable.
# Fix diabetes
attach(AEDB)
AEDB[,"DiabetesStatus"] <- NA
AEDB$DiabetesStatus[DM.composite == -999] <- NA
AEDB$DiabetesStatus[DM.composite == 0] <- "Control (no Diabetes Dx/Med)"
AEDB$DiabetesStatus[DM.composite == 1] <- "Diabetes"
detach(AEDB)
AEDB.temp <- subset(AEDB, select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "DM.composite", "DiabetesStatus"))
require(labelled)
AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
AEDB.temp$DiabetesStatus <- to_factor(AEDB.temp$DiabetesStatus)
DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
rm(AEDB.temp)
Athero-Express Biobank Study
Baseline characteristics
Showing the baseline table of the whole Athero-Express Biobank.
# Create baseline tables
# http://rstudio-pubs-static.s3.amazonaws.com/13321_da314633db924dc78986a850813a50d5.html
AEDB.CEA.tableOne = print(CreateTableOne(vars = basetable_vars,
# factorVars = basetable_bin,
# strata = "Symptoms.4g",
data = AEDB.CEA, includeNA = TRUE),
nonnormal = c(), missing = TRUE,
quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE,
format = "pf",
contDigits = 3)[,1:2]
level Overall
n 2388
Hospital % (freq) St. Antonius, Nieuwegein 39.8 ( 951)
UMC Utrecht 60.2 (1437)
Age (mean (SD)) 69.119 (9.206)
Gender % (freq) female 30.4 ( 727)
male 69.6 (1661)
TC_final (mean (SD)) 4.790 (1.464)
LDL_final (mean (SD)) 2.813 (1.082)
HDL_final (mean (SD)) 1.201 (0.443)
TG_final (mean (SD)) 1.704 (1.038)
hsCRP_plasma (mean (SD)) 20.175 (231.417)
systolic (mean (SD)) 152.714 (25.196)
diastoli (mean (SD)) 81.485 (25.318)
GFR_MDRD (mean (SD)) 73.022 (21.193)
BMI (mean (SD)) 26.503 (3.957)
KDOQI % (freq) No data available/missing 0.0 ( 0)
Normal kidney function 18.8 ( 450)
CKD 2 (Mild) 51.2 (1223)
CKD 3 (Moderate) 22.9 ( 546)
CKD 4 (Severe) 1.3 ( 31)
CKD 5 (Failure) 0.4 ( 10)
<NA> 5.4 ( 128)
BMI_WHO % (freq) No data available/missing 0.0 ( 0)
Underweight 1.0 ( 23)
Normal 34.2 ( 816)
Overweight 43.3 (1033)
Obese 14.4 ( 343)
<NA> 7.2 ( 173)
SmokerCurrent % (freq) no data available/missing 0.0 ( 0)
no 65.1 (1555)
yes 33.5 ( 801)
<NA> 1.3 ( 32)
eCigarettes (mean (SD)) 173121.383 (152646.899)
ePackYearsSmoking (mean (SD)) 23.715 (20.911)
DiabetesStatus % (freq) Control (no Diabetes Dx/Med) 76.0 (1815)
Diabetes 23.8 ( 568)
<NA> 0.2 ( 5)
Hypertension.composite % (freq) No data available/missing 0.0 ( 0)
no 14.7 ( 351)
yes 85.1 (2031)
<NA> 0.3 ( 6)
Hypertension.drugs % (freq) No data available/missing 0.0 ( 0)
no 23.6 ( 564)
yes 76.0 (1814)
<NA> 0.4 ( 10)
Med.anticoagulants % (freq) No data available/missing 0.0 ( 0)
no 88.1 (2104)
yes 11.3 ( 271)
<NA> 0.5 ( 13)
Med.all.antiplatelet % (freq) No data available/missing 0.0 ( 0)
no 12.4 ( 295)
yes 87.1 (2080)
<NA> 0.5 ( 13)
Med.Statin.LLD % (freq) No data available/missing 0.0 ( 0)
no 20.6 ( 492)
yes 78.9 (1885)
<NA> 0.5 ( 11)
Stroke_Dx % (freq) Missing 0.0 ( 0)
No stroke diagnosed 71.0 (1696)
Stroke diagnosed 20.9 ( 500)
<NA> 8.0 ( 192)
sympt % (freq) missing 0.0 ( 0)
Asymptomatic 11.1 ( 266)
TIA 39.5 ( 944)
minor stroke 16.8 ( 402)
Major stroke 9.6 ( 229)
Amaurosis fugax 15.5 ( 369)
Four vessel disease 1.6 ( 38)
Vertebrobasilary TIA 0.2 ( 5)
Retinal infarction 1.4 ( 33)
Symptomatic, but aspecific symtoms 2.2 ( 52)
Contralateral symptomatic occlusion 0.5 ( 11)
retinal infarction 0.3 ( 6)
armclaudication due to occlusion subclavian artery, CEA needed for bypass 0.0 ( 1)
retinal infarction + TIAs 0.1 ( 2)
Ocular ischemic syndrome 0.8 ( 18)
ischemisch glaucoom 0.0 ( 0)
subclavian steal syndrome 0.0 ( 1)
TGA 0.0 ( 0)
<NA> 0.5 ( 11)
Symptoms.5G % (freq) Asymptomatic 11.1 ( 266)
Ocular 16.2 ( 387)
Other 4.3 ( 103)
Retinal infarction 1.6 ( 39)
Stroke 26.4 ( 631)
TIA 39.8 ( 951)
<NA> 0.5 ( 11)
AsymptSympt % (freq) Asymptomatic 11.1 ( 266)
Ocular and others 22.2 ( 529)
Symptomatic 66.2 (1582)
<NA> 0.5 ( 11)
restenos % (freq) missing 0.0 ( 0)
de novo 93.6 (2235)
restenosis 4.9 ( 117)
stenose bij angioseal na PTCA 0.0 ( 0)
<NA> 1.5 ( 36)
stenose % (freq) missing 0.0 ( 0)
0-49% 0.5 ( 13)
50-70% 7.6 ( 181)
70-90% 46.9 (1121)
90-99% 38.4 ( 918)
100% (Occlusion) 1.5 ( 35)
NA 0.0 ( 0)
50-99% 0.6 ( 14)
70-99% 2.3 ( 55)
99 0.1 ( 2)
<NA> 2.1 ( 49)
EP_composite % (freq) No data available. 0.0 ( 0)
No composite endpoints 70.5 (1684)
Composite endpoints 24.2 ( 578)
<NA> 5.3 ( 126)
EP_composite_time (mean (SD)) 2.494 (1.102)
macmean0 (mean (SD)) 0.769 (1.186)
smcmean0 (mean (SD)) 1.982 (2.378)
Macrophages.bin % (freq) no/minor 35.6 ( 850)
moderate/heavy 41.7 ( 995)
<NA> 22.7 ( 543)
SMC.bin % (freq) no/minor 25.3 ( 605)
moderate/heavy 52.2 (1247)
<NA> 22.4 ( 536)
neutrophils (mean (SD)) 146.685 (419.386)
Mast_cells_plaque (mean (SD)) 164.488 (163.771)
IPH.bin % (freq) no 31.3 ( 747)
yes 46.5 (1111)
<NA> 22.2 ( 530)
vessel_density_averaged (mean (SD)) 8.322 (6.386)
Calc.bin % (freq) no/minor 42.1 (1005)
moderate/heavy 35.7 ( 852)
<NA> 22.2 ( 531)
Collagen.bin % (freq) no/minor 16.0 ( 381)
moderate/heavy 61.6 (1472)
<NA> 22.4 ( 535)
Fat.bin_10 % (freq) <10% 22.7 ( 541)
>10% 55.3 (1321)
<NA> 22.0 ( 526)
Fat.bin_40 % (freq) <40% 57.2 (1365)
>40% 20.8 ( 497)
<NA> 22.0 ( 526)
OverallPlaquePhenotype % (freq) atheromatous 20.1 ( 481)
fibroatheromatous 28.4 ( 677)
fibrous 29.1 ( 696)
<NA> 22.4 ( 534)
IL6_pg_ug_2015 (mean (SD)) 0.138 (0.556)
MCP1_pg_ug_2015 (mean (SD)) 0.612 (0.904)
Missing
n
Hospital % (freq) 0.0
Age (mean (SD)) 0.0
Gender % (freq) 0.0
TC_final (mean (SD)) 38.7
LDL_final (mean (SD)) 46.4
HDL_final (mean (SD)) 42.4
TG_final (mean (SD)) 43.5
hsCRP_plasma (mean (SD)) 52.1
systolic (mean (SD)) 11.6
diastoli (mean (SD)) 11.6
GFR_MDRD (mean (SD)) 5.3
BMI (mean (SD)) 7.2
KDOQI % (freq) 5.4
BMI_WHO % (freq) 7.2
SmokerCurrent % (freq) 1.3
eCigarettes (mean (SD)) 12.3
ePackYearsSmoking (mean (SD)) 12.3
DiabetesStatus % (freq) 0.2
Hypertension.composite % (freq) 0.3
Hypertension.drugs % (freq) 0.4
Med.anticoagulants % (freq) 0.5
Med.all.antiplatelet % (freq) 0.5
Med.Statin.LLD % (freq) 0.5
Stroke_Dx % (freq) 8.0
sympt % (freq) 0.5
Symptoms.5G % (freq) 0.5
AsymptSympt % (freq) 0.5
restenos % (freq) 1.5
stenose % (freq) 2.1
EP_composite % (freq) 5.3
EP_composite_time (mean (SD)) 5.4
macmean0 (mean (SD)) 28.4
smcmean0 (mean (SD)) 28.6
Macrophages.bin % (freq) 22.7
SMC.bin % (freq) 22.4
neutrophils (mean (SD)) 87.2
Mast_cells_plaque (mean (SD)) 89.9
IPH.bin % (freq) 22.2
vessel_density_averaged (mean (SD)) 34.0
Calc.bin % (freq) 22.2
Collagen.bin % (freq) 22.4
Fat.bin_10 % (freq) 22.0
Fat.bin_40 % (freq) 22.0
OverallPlaquePhenotype % (freq) 22.4
IL6_pg_ug_2015 (mean (SD)) 51.7
MCP1_pg_ug_2015 (mean (SD)) 49.7 AEDB.CEA.subset <- subset(AEDB.CEA, !is.na(IL6_pg_ug_2015) | !is.na(MCP1_pg_ug_2015))
AEDB.CEA.subset.tableOne = print(CreateTableOne(vars = basetable_vars,
# factorVars = basetable_bin,
# strata = "DiabetesStatus",
data = AEDB.CEA.subset, includeNA = TRUE),
nonnormal = c(), missing = TRUE,
quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE,
format = "pf",
contDigits = 3)[,1:2]
level Overall
n 1201
Hospital % (freq) St. Antonius, Nieuwegein 46.9 ( 563)
UMC Utrecht 53.1 ( 638)
Age (mean (SD)) 68.674 (9.167)
Gender % (freq) female 30.6 ( 367)
male 69.4 ( 834)
TC_final (mean (SD)) 4.730 (1.250)
LDL_final (mean (SD)) 2.812 (1.055)
HDL_final (mean (SD)) 1.180 (0.470)
TG_final (mean (SD)) 1.656 (0.964)
hsCRP_plasma (mean (SD)) 15.273 (107.100)
systolic (mean (SD)) 155.628 (26.036)
diastoli (mean (SD)) 82.644 (13.533)
GFR_MDRD (mean (SD)) 71.764 (20.074)
BMI (mean (SD)) 26.355 (3.706)
KDOQI % (freq) No data available/missing 0.0 ( 0)
Normal kidney function 17.2 ( 207)
CKD 2 (Mild) 53.0 ( 636)
CKD 3 (Moderate) 24.7 ( 297)
CKD 4 (Severe) 1.1 ( 13)
CKD 5 (Failure) 0.4 ( 5)
<NA> 3.6 ( 43)
BMI_WHO % (freq) No data available/missing 0.0 ( 0)
Underweight 0.9 ( 11)
Normal 35.2 ( 423)
Overweight 46.7 ( 561)
Obese 12.7 ( 153)
<NA> 4.4 ( 53)
SmokerCurrent % (freq) no data available/missing 0.0 ( 0)
no 63.0 ( 757)
yes 35.4 ( 425)
<NA> 1.6 ( 19)
eCigarettes (mean (SD)) 171480.910 (153112.295)
ePackYearsSmoking (mean (SD)) 23.491 (20.974)
DiabetesStatus % (freq) Control (no Diabetes Dx/Med) 77.2 ( 927)
Diabetes 22.8 ( 274)
Hypertension.composite % (freq) No data available/missing 0.0 ( 0)
no 13.8 ( 166)
yes 86.2 (1035)
Hypertension.drugs % (freq) No data available/missing 0.0 ( 0)
no 22.5 ( 270)
yes 77.4 ( 929)
<NA> 0.2 ( 2)
Med.anticoagulants % (freq) No data available/missing 0.0 ( 0)
no 88.0 (1057)
yes 11.8 ( 142)
<NA> 0.2 ( 2)
Med.all.antiplatelet % (freq) No data available/missing 0.0 ( 0)
no 10.5 ( 126)
yes 89.1 (1070)
<NA> 0.4 ( 5)
Med.Statin.LLD % (freq) No data available/missing 0.0 ( 0)
no 21.9 ( 263)
yes 77.9 ( 936)
<NA> 0.2 ( 2)
Stroke_Dx % (freq) Missing 0.0 ( 0)
No stroke diagnosed 75.6 ( 908)
Stroke diagnosed 19.1 ( 229)
<NA> 5.3 ( 64)
sympt % (freq) missing 0.0 ( 0)
Asymptomatic 10.9 ( 131)
TIA 41.2 ( 495)
minor stroke 14.8 ( 178)
Major stroke 10.8 ( 130)
Amaurosis fugax 15.2 ( 183)
Four vessel disease 1.9 ( 23)
Vertebrobasilary TIA 0.2 ( 2)
Retinal infarction 1.2 ( 15)
Symptomatic, but aspecific symtoms 2.4 ( 29)
Contralateral symptomatic occlusion 0.5 ( 6)
retinal infarction 0.2 ( 3)
armclaudication due to occlusion subclavian artery, CEA needed for bypass 0.1 ( 1)
retinal infarction + TIAs 0.0 ( 0)
Ocular ischemic syndrome 0.1 ( 1)
ischemisch glaucoom 0.0 ( 0)
subclavian steal syndrome 0.0 ( 0)
TGA 0.0 ( 0)
<NA> 0.3 ( 4)
Symptoms.5G % (freq) Asymptomatic 10.9 ( 131)
Ocular 15.3 ( 184)
Other 4.9 ( 59)
Retinal infarction 1.5 ( 18)
Stroke 25.6 ( 308)
TIA 41.4 ( 497)
<NA> 0.3 ( 4)
AsymptSympt % (freq) Asymptomatic 10.9 ( 131)
Ocular and others 21.7 ( 261)
Symptomatic 67.0 ( 805)
<NA> 0.3 ( 4)
restenos % (freq) missing 0.0 ( 0)
de novo 94.6 (1136)
restenosis 3.1 ( 37)
stenose bij angioseal na PTCA 0.0 ( 0)
<NA> 2.3 ( 28)
stenose % (freq) missing 0.0 ( 0)
0-49% 0.5 ( 6)
50-70% 6.0 ( 72)
70-90% 45.3 ( 544)
90-99% 42.6 ( 512)
100% (Occlusion) 0.8 ( 10)
NA 0.0 ( 0)
50-99% 0.4 ( 5)
70-99% 1.2 ( 14)
99 0.0 ( 0)
<NA> 3.2 ( 38)
EP_composite % (freq) No data available. 0.0 ( 0)
No composite endpoints 73.5 ( 883)
Composite endpoints 25.6 ( 308)
<NA> 0.8 ( 10)
EP_composite_time (mean (SD)) 2.623 (1.112)
macmean0 (mean (SD)) 0.790 (1.220)
smcmean0 (mean (SD)) 1.934 (2.185)
Macrophages.bin % (freq) no/minor 47.5 ( 570)
moderate/heavy 50.6 ( 608)
<NA> 1.9 ( 23)
SMC.bin % (freq) no/minor 31.2 ( 375)
moderate/heavy 66.9 ( 804)
<NA> 1.8 ( 22)
neutrophils (mean (SD)) 170.898 (479.879)
Mast_cells_plaque (mean (SD)) 172.458 (173.290)
IPH.bin % (freq) no 38.3 ( 460)
yes 60.0 ( 720)
<NA> 1.7 ( 21)
vessel_density_averaged (mean (SD)) 8.426 (6.475)
Calc.bin % (freq) no/minor 50.1 ( 602)
moderate/heavy 48.1 ( 578)
<NA> 1.7 ( 21)
Collagen.bin % (freq) no/minor 20.7 ( 249)
moderate/heavy 77.7 ( 933)
<NA> 1.6 ( 19)
Fat.bin_10 % (freq) <10% 26.8 ( 322)
>10% 71.6 ( 860)
<NA> 1.6 ( 19)
Fat.bin_40 % (freq) <40% 71.6 ( 860)
>40% 26.8 ( 322)
<NA> 1.6 ( 19)
OverallPlaquePhenotype % (freq) atheromatous 26.7 ( 321)
fibroatheromatous 35.9 ( 431)
fibrous 35.6 ( 427)
<NA> 1.8 ( 22)
IL6_pg_ug_2015 (mean (SD)) 0.138 (0.556)
MCP1_pg_ug_2015 (mean (SD)) 0.612 (0.904)
Missing
n
Hospital % (freq) 0.0
Age (mean (SD)) 0.0
Gender % (freq) 0.0
TC_final (mean (SD)) 33.6
LDL_final (mean (SD)) 39.8
HDL_final (mean (SD)) 36.6
TG_final (mean (SD)) 36.2
hsCRP_plasma (mean (SD)) 38.8
systolic (mean (SD)) 14.0
diastoli (mean (SD)) 14.0
GFR_MDRD (mean (SD)) 3.5
BMI (mean (SD)) 4.2
KDOQI % (freq) 3.6
BMI_WHO % (freq) 4.4
SmokerCurrent % (freq) 1.6
eCigarettes (mean (SD)) 9.8
ePackYearsSmoking (mean (SD)) 9.8
DiabetesStatus % (freq) 0.0
Hypertension.composite % (freq) 0.0
Hypertension.drugs % (freq) 0.2
Med.anticoagulants % (freq) 0.2
Med.all.antiplatelet % (freq) 0.4
Med.Statin.LLD % (freq) 0.2
Stroke_Dx % (freq) 5.3
sympt % (freq) 0.3
Symptoms.5G % (freq) 0.3
AsymptSympt % (freq) 0.3
restenos % (freq) 2.3
stenose % (freq) 3.2
EP_composite % (freq) 0.8
EP_composite_time (mean (SD)) 1.0
macmean0 (mean (SD)) 2.3
smcmean0 (mean (SD)) 2.7
Macrophages.bin % (freq) 1.9
SMC.bin % (freq) 1.8
neutrophils (mean (SD)) 82.0
Mast_cells_plaque (mean (SD)) 86.2
IPH.bin % (freq) 1.7
vessel_density_averaged (mean (SD)) 8.8
Calc.bin % (freq) 1.7
Collagen.bin % (freq) 1.6
Fat.bin_10 % (freq) 1.6
Fat.bin_40 % (freq) 1.6
OverallPlaquePhenotype % (freq) 1.8
IL6_pg_ug_2015 (mean (SD)) 3.9
MCP1_pg_ug_2015 (mean (SD)) 0.1 AEDB.CEA.subset.serum <- subset(AEDB.CEA, !is.na(IL6) | !is.na(MCP1))
AEDB.CEA.subset.serum.tableOne = print(CreateTableOne(vars = basetable_vars,
# factorVars = basetable_bin,
# strata = "DiabetesStatus",
data = AEDB.CEA.subset.serum, includeNA = TRUE),
nonnormal = c(), missing = TRUE,
quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE,
format = "pf",
contDigits = 3)[,1:2]
level Overall
n 593
Hospital % (freq) St. Antonius, Nieuwegein 64.6 (383)
UMC Utrecht 35.4 (210)
Age (mean (SD)) 67.297 (8.787)
Gender % (freq) female 28.8 (171)
male 71.2 (422)
TC_final (mean (SD)) 4.958 (1.228)
LDL_final (mean (SD)) 2.935 (1.064)
HDL_final (mean (SD)) 1.195 (0.394)
TG_final (mean (SD)) 1.810 (0.932)
hsCRP_plasma (mean (SD)) 15.379 (80.024)
systolic (mean (SD)) 156.674 (26.187)
diastoli (mean (SD)) 83.496 (12.532)
GFR_MDRD (mean (SD)) 70.569 (19.052)
BMI (mean (SD)) 26.528 (3.903)
KDOQI % (freq) No data available/missing 0.0 ( 0)
Normal kidney function 14.8 ( 88)
CKD 2 (Mild) 56.2 (333)
CKD 3 (Moderate) 25.6 (152)
CKD 4 (Severe) 1.0 ( 6)
CKD 5 (Failure) 0.5 ( 3)
<NA> 1.9 ( 11)
BMI_WHO % (freq) No data available/missing 0.0 ( 0)
Underweight 1.3 ( 8)
Normal 33.1 (196)
Overweight 45.2 (268)
Obese 13.5 ( 80)
<NA> 6.9 ( 41)
SmokerCurrent % (freq) no data available/missing 0.0 ( 0)
no 62.2 (369)
yes 36.6 (217)
<NA> 1.2 ( 7)
eCigarettes (mean (SD)) 172610.335 (143977.960)
ePackYearsSmoking (mean (SD)) 23.645 (19.723)
DiabetesStatus % (freq) Control (no Diabetes Dx/Med) 78.6 (466)
Diabetes 21.4 (127)
Hypertension.composite % (freq) No data available/missing 0.0 ( 0)
no 13.2 ( 78)
yes 86.8 (515)
Hypertension.drugs % (freq) No data available/missing 0.0 ( 0)
no 20.7 (123)
yes 78.9 (468)
<NA> 0.3 ( 2)
Med.anticoagulants % (freq) No data available/missing 0.0 ( 0)
no 86.8 (515)
yes 12.8 ( 76)
<NA> 0.3 ( 2)
Med.all.antiplatelet % (freq) No data available/missing 0.0 ( 0)
no 9.8 ( 58)
yes 89.9 (533)
<NA> 0.3 ( 2)
Med.Statin.LLD % (freq) No data available/missing 0.0 ( 0)
no 27.2 (161)
yes 72.5 (430)
<NA> 0.3 ( 2)
Stroke_Dx % (freq) Missing 0.0 ( 0)
No stroke diagnosed 76.6 (454)
Stroke diagnosed 17.0 (101)
<NA> 6.4 ( 38)
sympt % (freq) missing 0.0 ( 0)
Asymptomatic 15.7 ( 93)
TIA 41.1 (244)
minor stroke 17.0 (101)
Major stroke 6.7 ( 40)
Amaurosis fugax 13.5 ( 80)
Four vessel disease 2.2 ( 13)
Vertebrobasilary TIA 0.2 ( 1)
Retinal infarction 0.3 ( 2)
Symptomatic, but aspecific symtoms 3.0 ( 18)
Contralateral symptomatic occlusion 0.0 ( 0)
retinal infarction 0.0 ( 0)
armclaudication due to occlusion subclavian artery, CEA needed for bypass 0.0 ( 0)
retinal infarction + TIAs 0.0 ( 0)
Ocular ischemic syndrome 0.0 ( 0)
ischemisch glaucoom 0.0 ( 0)
subclavian steal syndrome 0.0 ( 0)
TGA 0.0 ( 0)
<NA> 0.2 ( 1)
Symptoms.5G % (freq) Asymptomatic 15.7 ( 93)
Ocular 13.5 ( 80)
Other 5.2 ( 31)
Retinal infarction 0.3 ( 2)
Stroke 23.8 (141)
TIA 41.3 (245)
<NA> 0.2 ( 1)
AsymptSympt % (freq) Asymptomatic 15.7 ( 93)
Ocular and others 19.1 (113)
Symptomatic 65.1 (386)
<NA> 0.2 ( 1)
restenos % (freq) missing 0.0 ( 0)
de novo 97.0 (575)
restenosis 3.0 ( 18)
stenose bij angioseal na PTCA 0.0 ( 0)
stenose % (freq) missing 0.0 ( 0)
0-49% 0.5 ( 3)
50-70% 3.4 ( 20)
70-90% 39.8 (236)
90-99% 54.8 (325)
100% (Occlusion) 1.3 ( 8)
NA 0.0 ( 0)
50-99% 0.0 ( 0)
70-99% 0.0 ( 0)
99 0.0 ( 0)
<NA> 0.2 ( 1)
EP_composite % (freq) No data available. 0.0 ( 0)
No composite endpoints 71.0 (421)
Composite endpoints 28.0 (166)
<NA> 1.0 ( 6)
EP_composite_time (mean (SD)) 2.616 (1.148)
macmean0 (mean (SD)) 0.962 (1.399)
smcmean0 (mean (SD)) 2.216 (2.485)
Macrophages.bin % (freq) no/minor 41.8 (248)
moderate/heavy 57.3 (340)
<NA> 0.8 ( 5)
SMC.bin % (freq) no/minor 29.5 (175)
moderate/heavy 69.8 (414)
<NA> 0.7 ( 4)
neutrophils (mean (SD)) 109.084 (261.117)
Mast_cells_plaque (mean (SD)) 159.436 (156.529)
IPH.bin % (freq) no 23.9 (142)
yes 75.9 (450)
<NA> 0.2 ( 1)
vessel_density_averaged (mean (SD)) 9.001 (5.629)
Calc.bin % (freq) no/minor 41.1 (244)
moderate/heavy 58.7 (348)
<NA> 0.2 ( 1)
Collagen.bin % (freq) no/minor 18.5 (110)
moderate/heavy 80.9 (480)
<NA> 0.5 ( 3)
Fat.bin_10 % (freq) <10% 19.6 (116)
>10% 80.3 (476)
<NA> 0.2 ( 1)
Fat.bin_40 % (freq) <40% 66.1 (392)
>40% 33.7 (200)
<NA> 0.2 ( 1)
OverallPlaquePhenotype % (freq) atheromatous 33.7 (200)
fibroatheromatous 36.6 (217)
fibrous 29.5 (175)
<NA> 0.2 ( 1)
IL6_pg_ug_2015 (mean (SD)) 0.177 (0.809)
MCP1_pg_ug_2015 (mean (SD)) 0.561 (1.083)
Missing
n
Hospital % (freq) 0.0
Age (mean (SD)) 0.0
Gender % (freq) 0.0
TC_final (mean (SD)) 19.2
LDL_final (mean (SD)) 29.8
HDL_final (mean (SD)) 24.8
TG_final (mean (SD)) 23.3
hsCRP_plasma (mean (SD)) 45.0
systolic (mean (SD)) 6.2
diastoli (mean (SD)) 6.2
GFR_MDRD (mean (SD)) 1.7
BMI (mean (SD)) 6.7
KDOQI % (freq) 1.9
BMI_WHO % (freq) 6.9
SmokerCurrent % (freq) 1.2
eCigarettes (mean (SD)) 9.4
ePackYearsSmoking (mean (SD)) 9.4
DiabetesStatus % (freq) 0.0
Hypertension.composite % (freq) 0.0
Hypertension.drugs % (freq) 0.3
Med.anticoagulants % (freq) 0.3
Med.all.antiplatelet % (freq) 0.3
Med.Statin.LLD % (freq) 0.3
Stroke_Dx % (freq) 6.4
sympt % (freq) 0.2
Symptoms.5G % (freq) 0.2
AsymptSympt % (freq) 0.2
restenos % (freq) 0.0
stenose % (freq) 0.2
EP_composite % (freq) 1.0
EP_composite_time (mean (SD)) 1.0
macmean0 (mean (SD)) 0.3
smcmean0 (mean (SD)) 1.0
Macrophages.bin % (freq) 0.8
SMC.bin % (freq) 0.7
neutrophils (mean (SD)) 77.9
Mast_cells_plaque (mean (SD)) 72.2
IPH.bin % (freq) 0.2
vessel_density_averaged (mean (SD)) 3.0
Calc.bin % (freq) 0.2
Collagen.bin % (freq) 0.5
Fat.bin_10 % (freq) 0.2
Fat.bin_40 % (freq) 0.2
OverallPlaquePhenotype % (freq) 0.2
IL6_pg_ug_2015 (mean (SD)) 24.6
MCP1_pg_ug_2015 (mean (SD)) 23.9 Writing the baseline table to Excel format.
# Write basetable
require(openxlsx)
write.xlsx(file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AE.BaselineTable.wholeCEA.xlsx"),
AEDB.CEA.tableOne,
row.names = TRUE,
col.names = TRUE,
sheetName = "wholeAEDB_Baseline")
write.xlsx(file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AE.BaselineTable.subsetCEA.xlsx"),
AEDB.CEA.subset.tableOne,
row.names = TRUE,
col.names = TRUE,
sheetName = "subsetAEDB_Baseline")
write.xlsx(file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AE.BaselineTable.subsetCEAserum.xlsx"),
AEDB.CEA.subset.serum.tableOne,
row.names = TRUE,
col.names = TRUE,
sheetName = "subsetAEDB_Baseline_serum")Athero-Express Genomics Study (AEGS)
We will add the samples which were genotyped in the Athero-Express Biobank Study, i.e. Athero-Express Genomics Study 1 (AEGS1, Affymetrix SNP 5.0), AEGS2 (Affymetrix Axiom CEU), and AEGS3 (Illumina GSA).
Loading AEGS data
AEGS1_2_3 <- fread(paste0(ROOT_loc,"/PLINK/_AE_Originals/AEGS_COMBINED_QC2018/aegs1_2_3_combo_postqcmichimp_n2493.forR.txt"),
verbose = TRUE, showProgress = TRUE)This installation of data.table has not been compiled with OpenMP support.
omp_get_num_procs() 1
R_DATATABLE_NUM_PROCS_PERCENT unset (default 50)
R_DATATABLE_NUM_THREADS unset
omp_get_thread_limit() 1
omp_get_max_threads() 1
OMP_THREAD_LIMIT unset
OMP_NUM_THREADS unset
RestoreAfterFork true
data.table is using 1 threads. See ?setDTthreads.
Input contains no \n. Taking this to be a filename to open
[01] Check arguments
Using 1 threads (omp_get_max_threads()=1, nth=1)
NAstrings = [<<NA>>]
None of the NAstrings look like numbers.
show progress = 1
0/1 column will be read as integer
[02] Opening the file
Opening file /Users/swvanderlaan/PLINK/_AE_Originals/AEGS_COMBINED_QC2018/aegs1_2_3_combo_postqcmichimp_n2493.forR.txt
File opened, size = 315.1KB (322657 bytes).
Memory mapped ok
[03] Detect and skip BOM
[04] Arrange mmap to be \0 terminated
\n has been found in the input and different lines can end with different line endings (e.g. mixed \n and \r\n in one file). This is common and ideal.
File ends abruptly with '4'. Final end-of-line is missing. Using cow page to write 0 to the last byte.
[05] Skipping initial rows if needed
Positioned on line 1 starting: <<FID_forQC IID_forQC SampleID_p>>
[06] Detect separator, quoting rule, and ncolumns
Detecting sep automatically ...
sep=0x9 with 100 lines of 14 fields using quote rule 0
Detected 14 columns on line 1. This line is either column names or first data row. Line starts as: <<FID_forQC IID_forQC SampleID_p>>
Quote rule picked = 0
fill=false and the most number of columns found is 14
[07] Detect column types, good nrow estimate and whether first row is column names
Number of sampling jump points = 10 because (322657 bytes from row 1 to eof) / (2 * 15634 jump0size) == 10
Type codes (jump 000) : AAAA55AAAAAAA5 Quote rule 0
Type codes (jump 010) : AAAAA5AAAAAAA5 Quote rule 0
'header' determined to be true due to column 6 containing a string on row 1 and a lower type (int32) in the rest of the 1070 sample rows
=====
Sampled 1070 rows (handled \n inside quoted fields) at 11 jump points
Bytes from first data row on line 2 to the end of last row: 322506
Line length: mean=132.61 sd=28.26 min=97 max=236
Estimated number of rows: 322506 / 132.61 = 2432
Initial alloc = 3324 rows (2432 + 36%) using bytes/max(mean-2*sd,min) clamped between [1.1*estn, 2.0*estn]
=====
[08] Assign column names
[09] Apply user overrides on column types
After 0 type and 0 drop user overrides : AAAAA5AAAAAAA5
[10] Allocate memory for the datatable
Allocating 14 column slots (14 - 0 dropped) with 3324 rows
[11] Read the data
jumps=[0..1), chunk_size=322506, total_size=322506
Read 2500 rows x 14 columns from 315.1KB (322657 bytes) file in 00:00.016 wall clock time
[12] Finalizing the datatable
Type counts:
2 : int32 '5'
12 : string 'A'
=============================
0.002s ( 11%) Memory map 0.000GB file
0.007s ( 44%) sep='\t' ncol=14 and header detection
0.000s ( 2%) Column type detection using 1070 sample rows
0.000s ( 2%) Allocation of 3324 rows x 14 cols (0.000GB) of which 2500 ( 75%) rows used
0.007s ( 41%) Reading 1 chunks (0 swept) of 0.308MB (each chunk 2500 rows) using 1 threads
+ 0.002s ( 14%) Parse to row-major thread buffers (grown 0 times)
+ 0.004s ( 25%) Transpose
+ 0.000s ( 2%) Waiting
0.000s ( 0%) Rereading 0 columns due to out-of-sample type exceptions
0.016s TotalAEGS1_2_3$Study_Number <- as.numeric(AEGS1_2_3$Study_Number)NAs introduced by coercionAEGS1_2_3$Age <- NULL
dim(AEGS1_2_3)[1] 2500 13head(AEGS1_2_3)
AEGS_raw <- merge(AEGS1_2_3, AEDB, by.x = "Study_Number", by.y = "STUDY_NUMBER", sort = FALSE,
all.x = TRUE)
dim(AEGS_raw)[1] 2500 1043warnings()
Here we will subset only those genotyped samples that passed genotyping quality control, are unrelated, and have informed consent.
AEGS_raw$Artery_summary <- to_factor(AEGS_raw$Artery_summary)
AEGS_raw$informedconsent <- to_factor(AEGS_raw$informedconsent)
table(AEGS_raw$Artery_summary, AEGS_raw$QC2018_FILTER)
family_discard family_keep issue passed
No artery known (yet), no surgery (patient ill, died, exited study), re-numbered to AAA 0 0 0 0
carotid (left & right) 22 21 326 1957
femoral/iliac (left, right or both sides) 1 0 17 109
other carotid arteries (common, external) 0 0 0 11
carotid bypass and injury (left, right or both sides) 0 0 0 1
aneurysmata (carotid & femoral) 0 0 0 0
aorta 0 0 0 0
other arteries (renal, popliteal, vertebral) 0 0 0 1
femoral bypass, angioseal and injury (left, right or both sides) 0 0 0 0table(AEGS_raw$informedconsent, AEGS_raw$QC2018_FILTER)
family_discard family_keep issue passed
missing 0 0 0 0
no, died 1 0 5 38
yes 17 16 262 1435
yes, health treatment when possible 2 2 41 322
yes, no health treatment 2 2 8 93
yes, no health treatment, no commercial business 0 0 0 15
yes, no tissue, no commerical business 0 0 0 0
yes, no tissue, no questionnaires, no medical info, no commercial business 0 0 0 0
yes, no questionnaires, no health treatment, no commercial business 0 0 0 1
yes, no questionnaires, health treatment when possible 0 0 1 2
yes, no tissue, no questionnaires, no health treatment, no commerical business 0 0 0 0
yes, no health treatment, no medical info, no commercial business 0 0 4 13
yes, no tissue, no questionnaires, no health treatment, no medical info, no commercial business 0 0 0 0
yes, no questionnaires, no health treatment 0 0 0 0
yes, no tissue, no health treatment 0 0 0 0
yes, no tissue, no questionnaires 0 0 0 0
yes, no tissue, health treatment when possible 0 0 0 0
yes, no tissue 0 0 0 0
yes, no commerical business 0 1 7 35
yes, health treatment when possible, no commercial business 0 0 0 25
yes, no medical info, no commercial business 0 0 1 4
yes, no questionnaires 0 0 0 1
yes, no tissue, no questionnaires, no health treatment, no medical info 0 0 0 0
yes, no tissue, no questionnaires, no health treatment, no commercial business 0 0 0 0
yes, no medical info 0 0 1 6
yes, no questionnaires, no commercial business 0 0 0 0
yes, no questionnaires, no health treatment, no medical info 0 0 0 1
yes, no questionnaires, health treatment when possible, no commercial business 0 0 0 0
yes, no health treatment, no medical info 0 0 0 5
no, doesn't want to 0 0 0 0
no, unable to sign 0 0 1 17
no, no reaction 0 0 6 14
no, lost 0 0 2 6
no, too old 0 0 4 18
yes, no medical info, health treatment when possible 0 0 0 2
no (never asked for IC because there was no tissue) 0 0 0 0
yes, no medical info, no commercial business, health treatment when possible 0 0 0 2
no, endpoint 0 0 0 0
wil niets invullen, wel alles gebruiken 0 0 0 7
second informed concents: yes, no commercial business 0 0 0 2
nooit geincludeerd 0 0 0 0AEGSselect <- subset(AEGS_raw,
QC2018_FILTER != "issue" & QC2018_FILTER != "family_discard" &
(Artery_summary == "carotid (left & right)" | Artery_summary == "other carotid arteries (common, external)") &
informedconsent != "missing" &
informedconsent != "no, died" &
informedconsent != "yes, no tissue, no commerical business" &
informedconsent != "yes, no tissue, no questionnaires, no medical info, no commercial business" &
informedconsent != "yes, no tissue, no questionnaires, no health treatment, no commerical business" &
informedconsent != "yes, no tissue, no questionnaires, no health treatment, no medical info, no commercial business" &
informedconsent != "yes, no tissue, no health treatment" &
informedconsent != "yes, no tissue, no questionnaires" &
informedconsent != "yes, no tissue, health treatment when possible" &
informedconsent != "yes, no tissue" &
informedconsent != "yes, no tissue, no questionnaires, no health treatment, no medical info" &
informedconsent != "yes, no tissue, no questionnaires, no health treatment, no commercial business" &
informedconsent != "no, doesn't want to" &
informedconsent != "no, unable to sign" &
informedconsent != "no, no reaction" &
informedconsent != "no, lost" &
informedconsent != "no, too old" &
informedconsent != "yes, no medical info, health treatment when possible" &
informedconsent != "no (never asked for IC because there was no tissue)" &
informedconsent != "no, endpoint" &
informedconsent != "nooit geincludeerd")
dim(AEGSselect)[1] 1889 1043table(AEGSselect$Artery_summary, AEGSselect$QC2018_FILTER)
family_keep passed
No artery known (yet), no surgery (patient ill, died, exited study), re-numbered to AAA 0 0
carotid (left & right) 21 1859
femoral/iliac (left, right or both sides) 0 0
other carotid arteries (common, external) 0 9
carotid bypass and injury (left, right or both sides) 0 0
aneurysmata (carotid & femoral) 0 0
aorta 0 0
other arteries (renal, popliteal, vertebral) 0 0
femoral bypass, angioseal and injury (left, right or both sides) 0 0table(AEGSselect$Artery_summary, AEGSselect$CHIP)
AffyAxiomCEU AffySNP5 IllGSA
No artery known (yet), no surgery (patient ill, died, exited study), re-numbered to AAA 0 0 0
carotid (left & right) 815 544 521
femoral/iliac (left, right or both sides) 0 0 0
other carotid arteries (common, external) 2 5 2
carotid bypass and injury (left, right or both sides) 0 0 0
aneurysmata (carotid & femoral) 0 0 0
aorta 0 0 0
other arteries (renal, popliteal, vertebral) 0 0 0
femoral bypass, angioseal and injury (left, right or both sides) 0 0 0table(AEGSselect$QC2018_FILTER, AEGSselect$CHIP)
AffyAxiomCEU AffySNP5 IllGSA
family_keep 8 1 12
passed 809 548 511table(AEGSselect$QC2018_FILTER, AEGSselect$SAMPLE_TYPE)
EDTA blood plaque unknown
family_keep 13 8 0
passed 1174 693 1AEDB.temp <- subset(AEGSselect, select = c("Study_Number", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "QC2018_FILTER", "CHIP", "SAMPLE_TYPE"))
require(labelled)
AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
AEDB.temp$QC2018_FILTER <- to_factor(AEDB.temp$QC2018_FILTER)
AEDB.temp$CHIP <- to_factor(AEDB.temp$CHIP)
AEDB.temp$SAMPLE_TYPE <- to_factor(AEDB.temp$SAMPLE_TYPE)
DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
rm(AEDB.temp)
Baseline characteristics
Showing the baseline table of the whole Athero-Express Genomics Study.
# Create baseline tables
# http://rstudio-pubs-static.s3.amazonaws.com/13321_da314633db924dc78986a850813a50d5.html
basetable_vars_geno = c("Hospital",
"Age", "Gender",
"TC_final", "LDL_final", "HDL_final", "TG_final",
"hsCRP_plasma",
"systolic", "diastoli", "GFR_MDRD", "BMI",
"KDOQI", "BMI_WHO",
"SmokerCurrent", "eCigarettes", "ePackYearsSmoking",
"DiabetesStatus", "Hypertension.composite",
"Hypertension.drugs", "Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD",
"Stroke_Dx", "sympt", "Symptoms.5G", "AsymptSympt",
"restenos", "stenose",
"EP_composite", "EP_composite_time",
"macmean0", "smcmean0", "Macrophages.bin", "SMC.bin",
"neutrophils", "Mast_cells_plaque",
"IPH.bin", "vessel_density_averaged",
"Calc.bin", "Collagen.bin",
"Fat.bin_10", "Fat.bin_40", "OverallPlaquePhenotype",
"IL6_pg_ug_2015", "MCP1_pg_ug_2015",
"QC2018_FILTER", "CHIP", "SAMPLE_TYPE")
basetable_bin_geno = c("Gender",
"KDOQI", "BMI_WHO",
"SmokerCurrent",
"DiabetesStatus", "Hypertension.composite",
"Hypertension.drugs", "Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD",
"Stroke_Dx", "sympt", "Symptoms.5G", "AsymptSympt",
"restenos", "stenose",
"EP_composite", "Macrophages.bin", "SMC.bin",
"IPH.bin",
"Calc.bin", "Collagen.bin",
"Fat.bin_10", "Fat.bin_40", "OverallPlaquePhenotype",
"QC2018_FILTER", "CHIP", "SAMPLE_TYPE")
basetable_con_geno = basetable_vars_geno[!basetable_vars_geno %in% basetable_bin_geno]
AEGSselect.tableOne = print(CreateTableOne(vars = basetable_vars_geno,
# factorVars = basetable_bin,
# strata = "DiabetesStatus",
data = AEGSselect, includeNA = TRUE),
nonnormal = c(), missing = TRUE,
quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE,
format = "pf",
contDigits = 3)[,1:2]
level Overall
n 1889
Hospital % (freq) St. Antonius, Nieuwegein 42.7 ( 807)
UMC Utrecht 57.3 (1082)
Age (mean (SD)) 69.143 (9.186)
Gender % (freq) female 31.8 ( 600)
male 68.2 (1289)
TC_final (mean (SD)) 4.764 (1.246)
LDL_final (mean (SD)) 2.802 (1.069)
HDL_final (mean (SD)) 1.203 (0.461)
TG_final (mean (SD)) 1.678 (0.994)
hsCRP_plasma (mean (SD)) 21.657 (243.628)
systolic (mean (SD)) 153.227 (25.351)
diastoli (mean (SD)) 81.799 (27.599)
GFR_MDRD (mean (SD)) 72.702 (20.665)
BMI (mean (SD)) 26.406 (3.890)
KDOQI % (freq) No data available/missing 0.0 ( 0)
Normal kidney function 18.3 ( 346)
CKD 2 (Mild) 52.5 ( 992)
CKD 3 (Moderate) 22.8 ( 430)
CKD 4 (Severe) 1.3 ( 24)
CKD 5 (Failure) 0.4 ( 7)
<NA> 4.8 ( 90)
BMI_WHO % (freq) No data available/missing 0.0 ( 0)
Underweight 1.0 ( 18)
Normal 35.8 ( 677)
Overweight 43.8 ( 827)
Obese 14.5 ( 273)
<NA> 5.0 ( 94)
SmokerCurrent % (freq) no data available/missing 0.0 ( 0)
no 65.7 (1242)
yes 33.1 ( 626)
<NA> 1.1 ( 21)
eCigarettes (mean (SD)) 168694.092 (149902.259)
ePackYearsSmoking (mean (SD)) 23.109 (20.535)
DiabetesStatus % (freq) Control (no Diabetes Dx/Med) 76.4 (1443)
Diabetes 23.6 ( 446)
Hypertension.composite % (freq) No data available/missing 0.0 ( 0)
no 14.1 ( 267)
yes 85.9 (1622)
Hypertension.drugs % (freq) No data available/missing 0.0 ( 0)
no 22.9 ( 433)
yes 77.0 (1454)
<NA> 0.1 ( 2)
Med.anticoagulants % (freq) No data available/missing 0.0 ( 0)
no 88.1 (1664)
yes 11.8 ( 223)
<NA> 0.1 ( 2)
Med.all.antiplatelet % (freq) No data available/missing 0.0 ( 0)
no 11.8 ( 222)
yes 88.0 (1663)
<NA> 0.2 ( 4)
Med.Statin.LLD % (freq) No data available/missing 0.0 ( 0)
no 20.6 ( 389)
yes 79.3 (1498)
<NA> 0.1 ( 2)
Stroke_Dx % (freq) Missing 0.0 ( 0)
No stroke diagnosed 72.3 (1366)
Stroke diagnosed 21.5 ( 407)
<NA> 6.1 ( 116)
sympt % (freq) missing 0.0 ( 0)
Asymptomatic 10.5 ( 199)
TIA 40.9 ( 773)
minor stroke 17.2 ( 324)
Major stroke 10.0 ( 189)
Amaurosis fugax 14.2 ( 268)
Four vessel disease 1.7 ( 32)
Vertebrobasilary TIA 0.2 ( 4)
Retinal infarction 1.5 ( 29)
Symptomatic, but aspecific symtoms 2.3 ( 44)
Contralateral symptomatic occlusion 0.3 ( 6)
retinal infarction 0.3 ( 5)
armclaudication due to occlusion subclavian artery, CEA needed for bypass 0.1 ( 1)
retinal infarction + TIAs 0.1 ( 2)
Ocular ischemic syndrome 0.2 ( 4)
ischemisch glaucoom 0.0 ( 0)
subclavian steal syndrome 0.1 ( 1)
TGA 0.0 ( 0)
<NA> 0.4 ( 8)
Symptoms.5G % (freq) Asymptomatic 10.5 ( 199)
Ocular 14.4 ( 272)
Other 4.4 ( 84)
Retinal infarction 1.8 ( 34)
Stroke 27.2 ( 513)
TIA 41.2 ( 779)
<NA> 0.4 ( 8)
AsymptSympt % (freq) Asymptomatic 10.5 ( 199)
Ocular and others 20.6 ( 390)
Symptomatic 68.4 (1292)
<NA> 0.4 ( 8)
restenos % (freq) missing 0.0 ( 0)
de novo 95.0 (1795)
restenosis 3.4 ( 65)
stenose bij angioseal na PTCA 0.0 ( 0)
<NA> 1.5 ( 29)
stenose % (freq) missing 0.0 ( 0)
0-49% 0.6 ( 11)
50-70% 7.0 ( 133)
70-90% 46.9 ( 885)
90-99% 39.9 ( 754)
100% (Occlusion) 1.0 ( 19)
NA 0.0 ( 0)
50-99% 0.4 ( 7)
70-99% 2.0 ( 38)
99 0.0 ( 0)
<NA> 2.2 ( 42)
EP_composite % (freq) No data available. 0.0 ( 0)
No composite endpoints 73.0 (1379)
Composite endpoints 24.7 ( 467)
<NA> 2.3 ( 43)
EP_composite_time (mean (SD)) 2.533 (1.091)
macmean0 (mean (SD)) 0.806 (1.235)
smcmean0 (mean (SD)) 1.983 (2.314)
Macrophages.bin % (freq) no/minor 37.0 ( 699)
moderate/heavy 45.5 ( 860)
<NA> 17.5 ( 330)
SMC.bin % (freq) no/minor 26.4 ( 498)
moderate/heavy 56.3 (1064)
<NA> 17.3 ( 327)
neutrophils (mean (SD)) 136.736 (276.117)
Mast_cells_plaque (mean (SD)) 161.412 (164.100)
IPH.bin % (freq) no 32.9 ( 622)
yes 50.0 ( 945)
<NA> 17.0 ( 322)
vessel_density_averaged (mean (SD)) 8.407 (6.362)
Calc.bin % (freq) no/minor 44.4 ( 838)
moderate/heavy 38.6 ( 730)
<NA> 17.0 ( 321)
Collagen.bin % (freq) no/minor 17.4 ( 329)
moderate/heavy 65.3 (1234)
<NA> 17.3 ( 326)
Fat.bin_10 % (freq) <10% 22.8 ( 430)
>10% 60.3 (1140)
<NA> 16.9 ( 319)
Fat.bin_40 % (freq) <40% 60.1 (1135)
>40% 23.0 ( 435)
<NA> 16.9 ( 319)
OverallPlaquePhenotype % (freq) atheromatous 22.3 ( 422)
fibroatheromatous 30.5 ( 576)
fibrous 30.1 ( 569)
<NA> 17.0 ( 322)
IL6_pg_ug_2015 (mean (SD)) 0.140 (0.570)
MCP1_pg_ug_2015 (mean (SD)) 0.611 (0.913)
QC2018_FILTER % (freq) family_keep 1.1 ( 21)
passed 98.9 (1868)
CHIP % (freq) AffyAxiomCEU 43.3 ( 817)
AffySNP5 29.1 ( 549)
IllGSA 27.7 ( 523)
SAMPLE_TYPE % (freq) EDTA blood 62.8 (1187)
plaque 37.1 ( 701)
unknown 0.1 ( 1)
Missing
n
Hospital % (freq) 0.0
Age (mean (SD)) 0.0
Gender % (freq) 0.0
TC_final (mean (SD)) 36.8
LDL_final (mean (SD)) 43.9
HDL_final (mean (SD)) 40.3
TG_final (mean (SD)) 41.2
hsCRP_plasma (mean (SD)) 45.5
systolic (mean (SD)) 11.8
diastoli (mean (SD)) 11.8
GFR_MDRD (mean (SD)) 4.7
BMI (mean (SD)) 4.9
KDOQI % (freq) 4.8
BMI_WHO % (freq) 5.0
SmokerCurrent % (freq) 1.1
eCigarettes (mean (SD)) 11.1
ePackYearsSmoking (mean (SD)) 11.1
DiabetesStatus % (freq) 0.0
Hypertension.composite % (freq) 0.0
Hypertension.drugs % (freq) 0.1
Med.anticoagulants % (freq) 0.1
Med.all.antiplatelet % (freq) 0.2
Med.Statin.LLD % (freq) 0.1
Stroke_Dx % (freq) 6.1
sympt % (freq) 0.4
Symptoms.5G % (freq) 0.4
AsymptSympt % (freq) 0.4
restenos % (freq) 1.5
stenose % (freq) 2.2
EP_composite % (freq) 2.3
EP_composite_time (mean (SD)) 2.4
macmean0 (mean (SD)) 22.6
smcmean0 (mean (SD)) 22.8
Macrophages.bin % (freq) 17.5
SMC.bin % (freq) 17.3
neutrophils (mean (SD)) 87.0
Mast_cells_plaque (mean (SD)) 88.8
IPH.bin % (freq) 17.0
vessel_density_averaged (mean (SD)) 27.9
Calc.bin % (freq) 17.0
Collagen.bin % (freq) 17.3
Fat.bin_10 % (freq) 16.9
Fat.bin_40 % (freq) 16.9
OverallPlaquePhenotype % (freq) 17.0
IL6_pg_ug_2015 (mean (SD)) 42.3
MCP1_pg_ug_2015 (mean (SD)) 40.0
QC2018_FILTER % (freq) 0.0
CHIP % (freq) 0.0
SAMPLE_TYPE % (freq) 0.0
AEGSselect.subset <- subset(AEGSselect, !is.na(IL6R_pg_ug_2015) | !is.na(MCP1_pg_ug_2015))
AEGSselect.subset.tableOne = print(CreateTableOne(vars = basetable_vars_geno,
# factorVars = basetable_bin,
# strata = "DiabetesStatus",
data = AEGSselect.subset, includeNA = TRUE),
nonnormal = c(), missing = TRUE,
quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE,
format = "pf",
contDigits = 3)[,1:2]
level Overall
n 1135
Hospital % (freq) St. Antonius, Nieuwegein 48.1 ( 546)
UMC Utrecht 51.9 ( 589)
Age (mean (SD)) 68.793 (9.141)
Gender % (freq) female 31.2 ( 354)
male 68.8 ( 781)
TC_final (mean (SD)) 4.716 (1.240)
LDL_final (mean (SD)) 2.794 (1.042)
HDL_final (mean (SD)) 1.181 (0.472)
TG_final (mean (SD)) 1.655 (0.966)
hsCRP_plasma (mean (SD)) 15.331 (108.739)
systolic (mean (SD)) 156.078 (25.938)
diastoli (mean (SD)) 82.773 (13.368)
GFR_MDRD (mean (SD)) 71.563 (19.944)
BMI (mean (SD)) 26.344 (3.729)
KDOQI % (freq) No data available/missing 0.0 ( 0)
Normal kidney function 16.7 ( 190)
CKD 2 (Mild) 53.8 ( 611)
CKD 3 (Moderate) 24.7 ( 280)
CKD 4 (Severe) 1.1 ( 13)
CKD 5 (Failure) 0.4 ( 5)
<NA> 3.2 ( 36)
BMI_WHO % (freq) No data available/missing 0.0 ( 0)
Underweight 1.0 ( 11)
Normal 35.3 ( 401)
Overweight 46.2 ( 524)
Obese 13.0 ( 147)
<NA> 4.6 ( 52)
SmokerCurrent % (freq) no data available/missing 0.0 ( 0)
no 63.2 ( 717)
yes 35.2 ( 400)
<NA> 1.6 ( 18)
eCigarettes (mean (SD)) 169723.220 (152110.961)
ePackYearsSmoking (mean (SD)) 23.250 (20.837)
DiabetesStatus % (freq) Control (no Diabetes Dx/Med) 77.4 ( 879)
Diabetes 22.6 ( 256)
Hypertension.composite % (freq) No data available/missing 0.0 ( 0)
no 13.7 ( 155)
yes 86.3 ( 980)
Hypertension.drugs % (freq) No data available/missing 0.0 ( 0)
no 22.3 ( 253)
yes 77.5 ( 880)
<NA> 0.2 ( 2)
Med.anticoagulants % (freq) No data available/missing 0.0 ( 0)
no 87.7 ( 995)
yes 12.2 ( 138)
<NA> 0.2 ( 2)
Med.all.antiplatelet % (freq) No data available/missing 0.0 ( 0)
no 10.7 ( 121)
yes 89.0 (1010)
<NA> 0.4 ( 4)
Med.Statin.LLD % (freq) No data available/missing 0.0 ( 0)
no 21.8 ( 247)
yes 78.1 ( 886)
<NA> 0.2 ( 2)
Stroke_Dx % (freq) Missing 0.0 ( 0)
No stroke diagnosed 75.9 ( 862)
Stroke diagnosed 18.7 ( 212)
<NA> 5.4 ( 61)
sympt % (freq) missing 0.0 ( 0)
Asymptomatic 10.7 ( 122)
TIA 41.6 ( 472)
minor stroke 15.1 ( 171)
Major stroke 10.8 ( 123)
Amaurosis fugax 14.6 ( 166)
Four vessel disease 1.9 ( 22)
Vertebrobasilary TIA 0.2 ( 2)
Retinal infarction 1.3 ( 15)
Symptomatic, but aspecific symtoms 2.4 ( 27)
Contralateral symptomatic occlusion 0.5 ( 6)
retinal infarction 0.3 ( 3)
armclaudication due to occlusion subclavian artery, CEA needed for bypass 0.1 ( 1)
retinal infarction + TIAs 0.0 ( 0)
Ocular ischemic syndrome 0.1 ( 1)
ischemisch glaucoom 0.0 ( 0)
subclavian steal syndrome 0.0 ( 0)
TGA 0.0 ( 0)
<NA> 0.4 ( 4)
Symptoms.5G % (freq) Asymptomatic 10.7 ( 122)
Ocular 14.7 ( 167)
Other 4.9 ( 56)
Retinal infarction 1.6 ( 18)
Stroke 25.9 ( 294)
TIA 41.8 ( 474)
<NA> 0.4 ( 4)
AsymptSympt % (freq) Asymptomatic 10.7 ( 122)
Ocular and others 21.2 ( 241)
Symptomatic 67.7 ( 768)
<NA> 0.4 ( 4)
restenos % (freq) missing 0.0 ( 0)
de novo 94.8 (1076)
restenosis 2.8 ( 32)
stenose bij angioseal na PTCA 0.0 ( 0)
<NA> 2.4 ( 27)
stenose % (freq) missing 0.0 ( 0)
0-49% 0.5 ( 6)
50-70% 5.6 ( 64)
70-90% 45.3 ( 514)
90-99% 43.3 ( 492)
100% (Occlusion) 0.9 ( 10)
NA 0.0 ( 0)
50-99% 0.3 ( 3)
70-99% 0.9 ( 10)
99 0.0 ( 0)
<NA> 3.2 ( 36)
EP_composite % (freq) No data available. 0.0 ( 0)
No composite endpoints 73.5 ( 834)
Composite endpoints 25.6 ( 291)
<NA> 0.9 ( 10)
EP_composite_time (mean (SD)) 2.624 (1.115)
macmean0 (mean (SD)) 0.811 (1.239)
smcmean0 (mean (SD)) 1.950 (2.206)
Macrophages.bin % (freq) no/minor 46.7 ( 530)
moderate/heavy 51.8 ( 588)
<NA> 1.5 ( 17)
SMC.bin % (freq) no/minor 31.3 ( 355)
moderate/heavy 67.3 ( 764)
<NA> 1.4 ( 16)
neutrophils (mean (SD)) 143.096 (279.963)
Mast_cells_plaque (mean (SD)) 171.063 (174.262)
IPH.bin % (freq) no 38.0 ( 431)
yes 60.7 ( 689)
<NA> 1.3 ( 15)
vessel_density_averaged (mean (SD)) 8.521 (6.460)
Calc.bin % (freq) no/minor 50.2 ( 570)
moderate/heavy 48.5 ( 551)
<NA> 1.2 ( 14)
Collagen.bin % (freq) no/minor 20.7 ( 235)
moderate/heavy 78.2 ( 888)
<NA> 1.1 ( 12)
Fat.bin_10 % (freq) <10% 26.3 ( 298)
>10% 72.6 ( 824)
<NA> 1.1 ( 13)
Fat.bin_40 % (freq) <40% 71.3 ( 809)
>40% 27.6 ( 313)
<NA> 1.1 ( 13)
OverallPlaquePhenotype % (freq) atheromatous 27.4 ( 311)
fibroatheromatous 36.2 ( 411)
fibrous 35.1 ( 398)
<NA> 1.3 ( 15)
IL6_pg_ug_2015 (mean (SD)) 0.140 (0.570)
MCP1_pg_ug_2015 (mean (SD)) 0.611 (0.913)
QC2018_FILTER % (freq) family_keep 0.4 ( 5)
passed 99.6 (1130)
CHIP % (freq) AffyAxiomCEU 54.3 ( 616)
AffySNP5 40.5 ( 460)
IllGSA 5.2 ( 59)
SAMPLE_TYPE % (freq) EDTA blood 55.9 ( 635)
plaque 44.0 ( 499)
unknown 0.1 ( 1)
Missing
n
Hospital % (freq) 0.0
Age (mean (SD)) 0.0
Gender % (freq) 0.0
TC_final (mean (SD)) 32.2
LDL_final (mean (SD)) 38.7
HDL_final (mean (SD)) 35.2
TG_final (mean (SD)) 34.9
hsCRP_plasma (mean (SD)) 38.0
systolic (mean (SD)) 14.4
diastoli (mean (SD)) 14.4
GFR_MDRD (mean (SD)) 3.1
BMI (mean (SD)) 4.4
KDOQI % (freq) 3.2
BMI_WHO % (freq) 4.6
SmokerCurrent % (freq) 1.6
eCigarettes (mean (SD)) 9.7
ePackYearsSmoking (mean (SD)) 9.7
DiabetesStatus % (freq) 0.0
Hypertension.composite % (freq) 0.0
Hypertension.drugs % (freq) 0.2
Med.anticoagulants % (freq) 0.2
Med.all.antiplatelet % (freq) 0.4
Med.Statin.LLD % (freq) 0.2
Stroke_Dx % (freq) 5.4
sympt % (freq) 0.4
Symptoms.5G % (freq) 0.4
AsymptSympt % (freq) 0.4
restenos % (freq) 2.4
stenose % (freq) 3.2
EP_composite % (freq) 0.9
EP_composite_time (mean (SD)) 1.1
macmean0 (mean (SD)) 1.8
smcmean0 (mean (SD)) 2.1
Macrophages.bin % (freq) 1.5
SMC.bin % (freq) 1.4
neutrophils (mean (SD)) 81.7
Mast_cells_plaque (mean (SD)) 86.0
IPH.bin % (freq) 1.3
vessel_density_averaged (mean (SD)) 8.0
Calc.bin % (freq) 1.2
Collagen.bin % (freq) 1.1
Fat.bin_10 % (freq) 1.1
Fat.bin_40 % (freq) 1.1
OverallPlaquePhenotype % (freq) 1.3
IL6_pg_ug_2015 (mean (SD)) 4.0
MCP1_pg_ug_2015 (mean (SD)) 0.1
QC2018_FILTER % (freq) 0.0
CHIP % (freq) 0.0
SAMPLE_TYPE % (freq) 0.0
Let’s also save these baseline tables.
# Write basetable
require(openxlsx)
write.xlsx(file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AEGS.BaselineTable.xlsx"),
AEGSselect.tableOne,
row.names = TRUE,
col.names = TRUE,
sheetName = "AEGS_Baseline")
write.xlsx(file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AEGS.BaselineTable.subset.xlsx"),
AEGSselect.subset.tableOne,
row.names = TRUE,
col.names = TRUE,
sheetName = "AEGS_Baseline_subset")
Data exploration
Here we inspect the data and when necessary transform quantitative measures. We will inspect the raw, natural log transformed + the smallest measurement, and inverse-normal transformation.
summary(AEDB.CEA$IL6_pg_ug_2015) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.0000 0.0157 0.0391 0.1376 0.0985 13.1882 1234 ggpubr::gghistogram(AEDB.CEA, "IL6_pg_ug_2015",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
add = "mean",
# rug = TRUE,
# add.params = list(color = "black", linetype = 2),
title = "IL6 plaque levels",
xlab = "pg/ug",
ggtheme = theme_minimal())Using `bins = 30` by default. Pick better value with the argument `bins`.
min_IL6pg_ug_2015 <- min(AEDB.CEA$IL6_pg_ug_2015, na.rm = TRUE)
min_IL6pg_ug_2015[1] 1.927428e-05AEDB.CEA$IL6_pg_ug_2015_LN <- log(AEDB.CEA$IL6_pg_ug_2015 + min_IL6pg_ug_2015)
ggpubr::gghistogram(AEDB.CEA, "IL6_pg_ug_2015_LN",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
add = "mean",
# rug = TRUE,
# add.params = list(color = "black", linetype = 2),
title = "IL6 plaque levels",
xlab = "natural log-transformed pg/ug",
ggtheme = theme_minimal())Using `bins = 30` by default. Pick better value with the argument `bins`.
AEDB.CEA$IL6_pg_ug_2015_rank <- qnorm((rank(AEDB.CEA$IL6_pg_ug_2015, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$IL6_pg_ug_2015)))
ggpubr::gghistogram(AEDB.CEA, "IL6_pg_ug_2015_rank",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
add = "mean",
# rug = TRUE,
# add.params = list(color = "black", linetype = 2),
title = "IL6 plaque levels",
xlab = "inverse-normal transformation pg/ug",
ggtheme = theme_minimal())Using `bins = 30` by default. Pick better value with the argument `bins`.
summary(AEDB.CEA$IL6R_pg_ug_2015) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.0000 0.0756 0.1472 0.2114 0.2526 4.0376 1232 ggpubr::gghistogram(AEDB.CEA, "IL6R_pg_ug_2015",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
add = "mean",
# rug = TRUE,
# add.params = list(color = "black", linetype = 2),
title = "IL6R plaque levels",
xlab = "pg/ug",
ggtheme = theme_minimal())Using `bins = 30` by default. Pick better value with the argument `bins`.
min_IL6R_pg_ug_2015 <- min(AEDB.CEA$IL6R_pg_ug_2015, na.rm = TRUE)
min_IL6R_pg_ug_2015[1] 0AEDB.CEA$IL6R_pg_ug_2015_LN <- log(AEDB.CEA$IL6R_pg_ug_2015 + min_IL6R_pg_ug_2015)
ggpubr::gghistogram(AEDB.CEA, "IL6R_pg_ug_2015_LN",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
add = "mean",
# rug = TRUE,
# add.params = list(color = "black", linetype = 2),
title = "IL6R plaque levels",
xlab = "natural log-transformed pg/ug",
ggtheme = theme_minimal())Using `bins = 30` by default. Pick better value with the argument `bins`.
AEDB.CEA$IL6R_pg_ug_2015_rank <- qnorm((rank(AEDB.CEA$IL6R_pg_ug_2015, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$IL6R_pg_ug_2015)))
ggpubr::gghistogram(AEDB.CEA, "IL6R_pg_ug_2015_rank",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
add = "mean",
# rug = TRUE,
# add.params = list(color = "black", linetype = 2),
title = "IL6R plaque levels",
xlab = "inverse-normal transformation pg/ug",
ggtheme = theme_minimal())Using `bins = 30` by default. Pick better value with the argument `bins`.
summary(AEDB.CEA$MCP1_pg_ug_2015) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.0005 0.1376 0.3407 0.6120 0.7240 10.8540 1188 ggpubr::gghistogram(AEDB.CEA, "MCP1_pg_ug_2015",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
add = "mean",
# rug = TRUE,
# add.params = list(color = "black", linetype = 2),
title = "MCP1 plaque levels",
xlab = "pg/ug",
ggtheme = theme_minimal())Using `bins = 30` by default. Pick better value with the argument `bins`.
min_MCP1_pg_ug_2015 <- min(AEDB.CEA$MCP1_pg_ug_2015, na.rm = TRUE)
min_MCP1_pg_ug_2015[1] 0.0004584575AEDB.CEA$MCP1_pg_ug_2015_LN <- log(AEDB.CEA$MCP1_pg_ug_2015 + min_MCP1_pg_ug_2015)
ggpubr::gghistogram(AEDB.CEA, "MCP1_pg_ug_2015_LN",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
add = "mean",
# rug = TRUE,
# add.params = list(color = "black", linetype = 2),
title = "MCP1 plaque levels",
xlab = "natural log-transformed pg/ug",
ggtheme = theme_minimal())Using `bins = 30` by default. Pick better value with the argument `bins`.
AEDB.CEA$MCP1_pg_ug_2015_rank <- qnorm((rank(AEDB.CEA$MCP1_pg_ug_2015, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$MCP1_pg_ug_2015)))
ggpubr::gghistogram(AEDB.CEA, "MCP1_pg_ug_2015_rank",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
add = "mean",
# rug = TRUE,
# add.params = list(color = "black", linetype = 2),
title = "MCP1 plaque levels",
xlab = "inverse-normal transformation pg/ug",
ggtheme = theme_minimal())Using `bins = 30` by default. Pick better value with the argument `bins`.
summary(AEDB.CEA$IL6) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.00 18.31 42.24 98.62 91.53 4586.07 1859 ggpubr::gghistogram(AEDB.CEA, "IL6",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
add = "mean",
# rug = TRUE,
# add.params = list(color = "black", linetype = 2),
title = "IL6 serum levels",
xlab = "pg/mL",
ggtheme = theme_minimal())Using `bins = 30` by default. Pick better value with the argument `bins`.
min_IL6 <- min(AEDB.CEA$IL6, na.rm = TRUE)
min_IL6[1] 0AEDB.CEA$IL6_LN <- log(AEDB.CEA$IL6 + min_IL6)
ggpubr::gghistogram(AEDB.CEA, "IL6_LN",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
add = "mean",
# rug = TRUE,
# add.params = list(color = "black", linetype = 2),
title = "IL6 serum levels",
xlab = "natural log-transformed pg/ug",
ggtheme = theme_minimal())Using `bins = 30` by default. Pick better value with the argument `bins`.
AEDB.CEA$IL6_rank <- qnorm((rank(AEDB.CEA$IL6, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$IL6)))
ggpubr::gghistogram(AEDB.CEA, "IL6_rank",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
add = "mean",
# rug = TRUE,
# add.params = list(color = "black", linetype = 2),
title = "IL6 serum levels",
xlab = "inverse-normal transformation pg/ug",
ggtheme = theme_minimal())Using `bins = 30` by default. Pick better value with the argument `bins`.
summary(AEDB.CEA$MCP1) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.00 56.42 103.00 135.76 178.99 926.27 1823 ggpubr::gghistogram(AEDB.CEA, "MCP1",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
add = "mean",
# rug = TRUE,
# add.params = list(color = "black", linetype = 2),
title = "MCP1 serum levels",
xlab = "pg/mL",
ggtheme = theme_minimal())Using `bins = 30` by default. Pick better value with the argument `bins`.
min_MCP1 <- min(AEDB.CEA$MCP1, na.rm = TRUE)
min_MCP1[1] 0AEDB.CEA$MCP1_LN <- log(AEDB.CEA$MCP1 + min_MCP1)
ggpubr::gghistogram(AEDB.CEA, "MCP1_LN",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
add = "mean",
# rug = TRUE,
# add.params = list(color = "black", linetype = 2),
title = "MCP1 serum levels",
xlab = "natural log-transformed pg/ug",
ggtheme = theme_minimal())Using `bins = 30` by default. Pick better value with the argument `bins`.
AEDB.CEA$MCP1_rank <- qnorm((rank(AEDB.CEA$MCP1, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$MCP1)))
ggpubr::gghistogram(AEDB.CEA, "MCP1_rank",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
add = "mean",
# rug = TRUE,
# add.params = list(color = "black", linetype = 2),
title = "MCP1 serum levels",
xlab = "inverse-normal transformation pg/ug",
ggtheme = theme_minimal())Using `bins = 30` by default. Pick better value with the argument `bins`.
Preliminary conclusion data exploration
After discussion we decided to pursuit the following strategy. In line with the previous work by Marios Georgakis we will apply natural log transformation on all proteins and focus the analysis on:
- MCP1 (serum and plaque),
- IL6 (serum and plaque), and
- IL6R (in plaque).
Analyses
The analyses are focused on three elements:
- plaque vulnerability phenotypes
- clinical status at inclusion (symptoms)
- secondary clinical outcome during three (3) years of follow-up
Covariates & other variables
- Age (continuous in 1-year increment). [Age]
- Sex (male vs. female). [Gender]
- Presence of hypertension at baseline (defined either as history of hypertension, SBP ≥140 mm Hg, DBP ≥90 mm Hg, or prescription of antihypertensive medications). [Hypertension.composite]
- Presence of diabetes mellitus at baseline (defined either as a history of diabetes, administration of glucose lowering medication, HbA1c ≥6.5%, fasting glucose ≥126 mg/dl, .or random glucose levels ≥200 mg/dl). [DiabetesStatus]
- Smoking (current, ex-, never). [SmokerCurrent]
- LDL-C levels (continuous). [LDL_final]
- Use of lipid-lowering drugs. [Med.Statin.LLD]
- Use of antiplatelet drugs. [Med.all.antiplatelet]
- eGFR (continuous). [GFR_MDRD]
- BMI (continuous). [BMI]
- History of cardiovascular disease (stroke, coronary artery disease, peripheral artery disease). [CAD_history, Stroke_history, Peripheral.interv]
- Level of stenosis (50-70% vs. 70-99%). [stenose]
- Presenting symptoms (asymptomatic, ocular, TIA, or stroke). [Symptoms.5G]
- hsCRP circulating levels (ln-transformed, continuous). [hsCRP_plasma]
- IL-6 plaque levels (ln-transformed, continuous). [IL6_pg_ug_2015_LN]
Models
We will analyze the data through four different models
- Model 1: adjusted for age and sex
- Model 2: adjusted for age, sex, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis,
- Model 3: same to model 2, with additional adjustments for circulating CRP levels
- Model 4: same to model 2 with additional adjustment for IL6 levels in the plaque
A. Cross-sectional analysis plaque phenotypes
In the cross-sectional analysis of plaque and serum MCP1, IL6, and IL6R levels we will focus on the following plaque vulnerability phenotypes:
- Percentage of macrophages (continuous trait)
- Percentage of SMCs (continuous trait)
- Number of intraplaque microvessels per 3-4 hotspots (continuous trait)
- Presence of moderate/heavy calcifications (binary trait)
- Presence of moderate/heavy collagen content (binary trait)
- Presence of lipid core no/<10% vs. >10% (binary trait)
- Presence of intraplaque hemorrhage (binary trait)
Continous traits
# macrophages
cat("Summary of data.\n")Summary of data.summary(AEDB.CEA$macmean0) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.0000 0.0733 0.3133 0.7688 1.0000 15.1000 679 min_macmean <- min(AEDB.CEA$macmean0, na.rm = TRUE)
cat(paste0("\nMinimum value % macrophages: ",min_macmean,".\n"))
Minimum value % macrophages: 0.AEDB.CEA$Macrophages_LN <- log(AEDB.CEA$macmean0 + min_macmean)
ggpubr::gghistogram(AEDB.CEA, "Macrophages_LN",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
add = "median",
#add_density = TRUE,
rug = TRUE,
#add.params = list(color = "black", linetype = 2),
title = "% macrophages",
xlab = "natural log-transformed %",
ggtheme = theme_minimal())Using `bins = 30` by default. Pick better value with the argument `bins`.
AEDB.CEA$Macrophages_rank <- qnorm((rank(AEDB.CEA$macmean0, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$macmean0)))
ggpubr::gghistogram(AEDB.CEA, "Macrophages_rank",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
add = "median",
#add_density = TRUE,
rug = TRUE,
#add.params = list(color = "black", linetype = 2),
title = "% macrophages",
xlab = "inverse-rank normalized %",
ggtheme = theme_minimal())Using `bins = 30` by default. Pick better value with the argument `bins`.
# smooth muscle cells
cat("Summary of data.\n")Summary of data.summary(AEDB.CEA$macmean0) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.0000 0.0733 0.3133 0.7688 1.0000 15.1000 679 min_smcmean <- min(AEDB.CEA$smcmean0, na.rm = TRUE)
cat(paste0("\nMinimum value % smooth muscle cells: ",min_smcmean,".\n"))
Minimum value % smooth muscle cells: 0.AEDB.CEA$SMC_LN <- log(AEDB.CEA$smcmean0 + min_smcmean)
ggpubr::gghistogram(AEDB.CEA, "SMC_LN",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
add = "median",
#add_density = TRUE,
rug = TRUE,
#add.params = list(color = "black", linetype = 2),
title = "% smooth muscle cells",
xlab = "natural log-transformed %",
ggtheme = theme_minimal())Using `bins = 30` by default. Pick better value with the argument `bins`.
AEDB.CEA$SMC_rank <- qnorm((rank(AEDB.CEA$smcmean0, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$smcmean0)))
ggpubr::gghistogram(AEDB.CEA, "SMC_rank",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
add = "median",
#add_density = TRUE,
rug = TRUE,
#add.params = list(color = "black", linetype = 2),
title = "% smooth muscle cells",
xlab = "inverse-rank normalized %",
ggtheme = theme_minimal())Using `bins = 30` by default. Pick better value with the argument `bins`.
# vessel density
cat("Summary of data.\n")Summary of data.summary(AEDB.CEA$vessel_density_averaged) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.000 4.000 7.000 8.322 11.300 48.000 813 min_vesseldensity <- min(AEDB.CEA$vessel_density_averaged, na.rm = TRUE)
min_vesseldensity[1] 0cat(paste0("\nMinimum value number of intraplaque neovessels per 3-4 hotspots: ",min_vesseldensity,".\n"))
Minimum value number of intraplaque neovessels per 3-4 hotspots: 0.AEDB.CEA$VesselDensity_LN <- log(AEDB.CEA$vessel_density_averaged + min_vesseldensity)
ggpubr::gghistogram(AEDB.CEA, "VesselDensity_LN",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
add = "median",
#add_density = TRUE,
rug = TRUE,
#add.params = list(color = "black", linetype = 2),
title = "number of intraplaque neovessels per 3-4 hotspots",
xlab = "natural log-transformed number",
ggtheme = theme_minimal())Using `bins = 30` by default. Pick better value with the argument `bins`.
AEDB.CEA$VesselDensity_rank <- qnorm((rank(AEDB.CEA$vessel_density_averaged, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$vessel_density_averaged)))
ggpubr::gghistogram(AEDB.CEA, "VesselDensity_rank",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
add = "median",
#add_density = TRUE,
rug = TRUE,
#add.params = list(color = "black", linetype = 2),
title = "number of intraplaque neovessels per 3-4 hotspots",
xlab = "inverse-rank normalized number",
ggtheme = theme_minimal())Using `bins = 30` by default. Pick better value with the argument `bins`.
Binary traits
# calcification
cat("Summary of data.\n")Summary of data.summary(AEDB.CEA$Calc.bin) no/minor moderate/heavy NA's
1005 852 531 contrasts(AEDB.CEA$Calc.bin) moderate/heavy
no/minor 0
moderate/heavy 1AEDB.CEA$CalcificationPlaque <- as.factor(AEDB.CEA$Calc.bin)
df <- AEDB.CEA %>%
filter(!is.na(CalcificationPlaque)) %>%
group_by(Gender, CalcificationPlaque) %>%
summarise(counts = n())
ggpubr::ggbarplot(df, x = "CalcificationPlaque", y = "counts",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#DB003F", "#1290D9"),
label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
title = "Calcification",
xlab = "calcification",
ggtheme = theme_minimal())
rm(df)
# collagen
cat("Summary of data.\n")Summary of data.summary(AEDB.CEA$Collagen.bin) no/minor moderate/heavy NA's
381 1472 535 contrasts(AEDB.CEA$Collagen.bin) moderate/heavy
no/minor 0
moderate/heavy 1AEDB.CEA$CollagenPlaque <- as.factor(AEDB.CEA$Collagen.bin)
df <- AEDB.CEA %>%
filter(!is.na(CollagenPlaque)) %>%
group_by(Gender, CollagenPlaque) %>%
summarise(counts = n())
ggpubr::ggbarplot(df, x = "CollagenPlaque", y = "counts",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#DB003F", "#1290D9"),
label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
title = "Collagen",
xlab = "collagen",
ggtheme = theme_minimal())
rm(df)
# fat 10%
cat("Summary of data.\n")Summary of data.summary(AEDB.CEA$Fat.bin_10) <10% >10% NA's
541 1321 526 contrasts(AEDB.CEA$Fat.bin_10) >10%
<10% 0
>10% 1AEDB.CEA$Fat10Perc <- as.factor(AEDB.CEA$Fat.bin_10)
df <- AEDB.CEA %>%
filter(!is.na(Fat10Perc)) %>%
group_by(Gender, Fat10Perc) %>%
summarise(counts = n())
ggpubr::ggbarplot(df, x = "Fat10Perc", y = "counts",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#DB003F", "#1290D9"),
label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
title = "Intraplaque fat",
xlab = "intraplaque fat",
ggtheme = theme_minimal())
rm(df)
# IPH
cat("Summary of data.\n")Summary of data.summary(AEDB.CEA$IPH.bin) no yes NA's
747 1111 530 contrasts(AEDB.CEA$IPH.bin) yes
no 0
yes 1AEDB.CEA$IPH <- as.factor(AEDB.CEA$IPH.bin)
df <- AEDB.CEA %>%
filter(!is.na(IPH)) %>%
group_by(Gender, IPH) %>%
summarise(counts = n())
ggpubr::ggbarplot(df, x = "IPH", y = "counts",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#DB003F", "#1290D9"),
label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
title = "Intraplaque hemorrhage",
xlab = "intraplaque hemorrhage",
ggtheme = theme_minimal())
rm(df)
# Symptoms
cat("Summary of data.\n")Summary of data.summary(AEDB.CEA$AsymptSympt) Asymptomatic Ocular and others Symptomatic NA's
266 529 1582 11 contrasts(AEDB.CEA$AsymptSympt) Ocular and others Symptomatic
Asymptomatic 0 0
Ocular and others 1 0
Symptomatic 0 1AEDB.CEA$AsymptSympt <- as.factor(AEDB.CEA$AsymptSympt)
df <- AEDB.CEA %>%
filter(!is.na(AsymptSympt)) %>%
group_by(Gender, AsymptSympt) %>%
summarise(counts = n())
ggpubr::ggbarplot(df, x = "AsymptSympt", y = "counts",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#DB003F", "#1290D9"),
label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
title = "Symptoms",
xlab = "symptoms",
ggtheme = theme_minimal())
rm(df)
In this section we make some variables to assist with analysis.
AEDB.CEA.samplesize = nrow(AEDB.CEA)
TRAITS.PROTEIN = c("IL6_LN", "MCP1_LN", "IL6_pg_ug_2015_LN", "IL6R_pg_ug_2015_LN", "MCP1_pg_ug_2015_LN")
TRAITS.PROTEIN.RANK = c("IL6_rank", "MCP1_rank", "IL6_pg_ug_2015_rank", "IL6R_pg_ug_2015_rank", "MCP1_pg_ug_2015_rank")
TRAITS.CON = c("Macrophages_LN", "SMC_LN", "VesselDensity_LN")
TRAITS.CON.RANK = c("Macrophages_rank", "SMC_rank", "VesselDensity_rank")
TRAITS.BIN = c("CalcificationPlaque", "CollagenPlaque", "Fat10Perc", "IPH")
# "Hospital",
# "Age", "Gender",
# "TC_final", "LDL_final", "HDL_final", "TG_final",
# "systolic", "diastoli", "GFR_MDRD", "BMI",
# "KDOQI", "BMI_WHO",
# "SmokerCurrent", "eCigarettes", "ePackYearsSmoking",
# "DiabetesStatus", "Hypertension.composite",
# "Hypertension.drugs", "Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD",
# "Stroke_Dx", "sympt", "Symptoms.5G", "restenos",
# "EP_composite", "EP_composite_time",
# "macmean0", "smcmean0", "Macrophages.bin", "SMC.bin",
# "neutrophils", "Mast_cells_plaque",
# "IPH.bin", "vessel_density_averaged",
# "Calc.bin", "Collagen.bin",
# "Fat.bin_10", "Fat.bin_40", "OverallPlaquePhenotype",
# "IL6_pg_ug_2015", "MCP1_pg_ug_2015",
# "QC2018_FILTER", "CHIP", "SAMPLE_TYPE",
# "CAD_history", "Stroke_history", "Peripheral.interv",
# "stenose"
# 1. Age (continuous in 1-year increment). [Age]
# 2. Sex (male vs. female). [Gender]
# 3. Presence of hypertension at baseline (defined either as history of hypertension, SBP ≥140 mm Hg, DBP ≥90 mm Hg, or prescription of antihypertensive medications). [Hypertension.composite]
# 4. Presence of diabetes mellitus at baseline (defined either as a history of diabetes, administration of glucose lowering medication, HbA1c ≥6.5%, fasting glucose ≥126 mg/dl, .or random glucose levels ≥200 mg/dl). [DiabetesStatus]
# 5. Smoking (current, ex-, never). [SmokerCurrent]
# 6. LDL-C levels (continuous). [LDL_final]
# 7. Use of lipid-lowering drugs. [Med.Statin.LLD]
# 8. Use of antiplatelet drugs. [Med.all.antiplatelet]
# 9. eGFR (continuous). [GFR_MDRD]
# 10. BMI (continuous). [BMI]
# 11. History of cardiovascular disease (stroke, coronary artery disease, peripheral artery disease). [CAD_history, Stroke_history, Peripheral.interv]
# 12. Level of stenosis (50-70% vs. 70-99%). [stenose]
# 13. Presenting symptoms (asymptomatic, ocular, TIA, or stroke). [Symptoms.5G]
# 14. hsCRP circulating levels (ln-transformed, continuous). [hsCRP_plasma]
# 15. IL-6 plaque levels (ln-transformed, continuous). [IL6_pg_ug_2015_LN]
# Models
# Model 1: adjusted for age and sex
# Model 2: adjusted for age, sex, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis,
# Model 3: same to model 2, with additional adjustments for circulating CRP levels
# Model 4: same to model 2 with additional adjustment for IL6 levels in the plaque
COVARIATES_M1 = c("Age", "Gender")
COVARIATES_M2 = c(COVARIATES_M1,
"Hypertension.composite", "DiabetesStatus", "SmokerCurrent",
"Med.Statin.LLD", "Med.all.antiplatelet",
"GFR_MDRD", "BMI",
"CAD_history", "Stroke_history", "Peripheral.interv",
"stenose")
COVARIATES_M3 = c(COVARIATES_M2, "LDL_final")
COVARIATES_M4 = c(COVARIATES_M2, "hsCRP_plasma")
COVARIATES_M5 = c(COVARIATES_M2, "IL6_pg_ug_2015_LN")
COVARIATES_M5rank = c(COVARIATES_M2, "IL6_pg_ug_2015_rank")
Model 1
In this model we correct for Age and Gender.
Natural log-transformed data
First we use the natural-log transformed data.
Analysis of continuous/quantitative plaque traits as a function of serum/plaque IL6(R)/MCP1 levels.
GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")Running linear regression...for (protein in 1:length(TRAITS.PROTEIN)) {
PROTEIN = TRAITS.PROTEIN[protein]
cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
for (trait in 1:length(TRAITS.CON)) {
TRAIT = TRAITS.CON[trait]
cat(paste0("\n- processing ",TRAIT,"\n\n"))
currentDF <- as.data.frame(AEDB.CEA %>%
dplyr::select(., PROTEIN, TRAIT, COVARIATES_M1) %>%
filter(complete.cases(.))) %>%
filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
# for debug
# print(DT::datatable(currentDF))
# print(nrow(currentDF))
# print(str(currentDF))
### univariate
fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender, data = currentDF)
model_step <- stepAIC(fit, direction = "both", trace = FALSE)
print(model_step)
print(summary(fit))
GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
GLM.results.TEMP[1,] = GLM.CON(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
GLM.results = rbind(GLM.results, GLM.results.TEMP)
}
}
Analysis of IL6_LN.
- processing Macrophages_LNNote: Using an external vector in selections is ambiguous.
[34mℹ[39m Use `all_of(PROTEIN)` instead of `PROTEIN` to silence this message.
[34mℹ[39m See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
[90mThis message is displayed once per session.[39m
Note: Using an external vector in selections is ambiguous.
[34mℹ[39m Use `all_of(TRAIT)` instead of `TRAIT` to silence this message.
[34mℹ[39m See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
[90mThis message is displayed once per session.[39m
Note: Using an external vector in selections is ambiguous.
[34mℹ[39m Use `all_of(COVARIATES_M1)` instead of `COVARIATES_M1` to silence this message.
[34mℹ[39m See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
[90mThis message is displayed once per session.[39m
Call:
lm(formula = currentDF[, PROTEIN] ~ Gender, data = currentDF)
Coefficients:
(Intercept) Gendermale
4.1909 -0.2708
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.9465 -0.7303 -0.0195 0.6615 4.2982
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.1972956 0.4035182 10.402 <2e-16 ***
currentDF[, TRAIT] -0.0251343 0.0259780 -0.968 0.3338
Age -0.0006383 0.0058262 -0.110 0.9128
Gendermale -0.2645982 0.1132391 -2.337 0.0199 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.059 on 437 degrees of freedom
Multiple R-squared: 0.01508, Adjusted R-squared: 0.008318
F-statistic: 2.23 on 3 and 437 DF, p-value: 0.08402
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_LN ' with ' Macrophages_LN ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_LN
Trait/outcome.............: Macrophages_LN
Effect size...............: -0.025134
Standard error............: 0.025978
Odds ratio (effect size)..: 0.975
Lower 95% CI..............: 0.927
Upper 95% CI..............: 1.026
T-value...................: -0.96752
P-value...................: 0.3338192
R^2.......................: 0.01508
Adjusted r^2..............: 0.008318
Sample size of AE DB......: 2388
Sample size of model......: 441
Missing data %............: 81.53266
- processing SMC_LN
Call:
lm(formula = currentDF[, PROTEIN] ~ Gender, data = currentDF)
Coefficients:
(Intercept) Gendermale
4.2091 -0.2669
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.8987 -0.7242 -0.0527 0.6511 4.2160
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.314537 0.399343 10.804 <2e-16 ***
currentDF[, TRAIT] 0.007338 0.031360 0.234 0.8151
Age -0.001604 0.005778 -0.278 0.7815
Gendermale -0.263661 0.110671 -2.382 0.0176 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.045 on 442 degrees of freedom
Multiple R-squared: 0.01344, Adjusted R-squared: 0.006746
F-statistic: 2.008 on 3 and 442 DF, p-value: 0.1122
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_LN ' with ' SMC_LN ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_LN
Trait/outcome.............: SMC_LN
Effect size...............: 0.007338
Standard error............: 0.03136
Odds ratio (effect size)..: 1.007
Lower 95% CI..............: 0.947
Upper 95% CI..............: 1.071
T-value...................: 0.233991
P-value...................: 0.8151004
R^2.......................: 0.013442
Adjusted r^2..............: 0.006746
Sample size of AE DB......: 2388
Sample size of model......: 446
Missing data %............: 81.32328
- processing VesselDensity_LN
Call:
lm(formula = currentDF[, PROTEIN] ~ Gender, data = currentDF)
Coefficients:
(Intercept) Gendermale
4.1737 -0.2411
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.9272 -0.7067 -0.0407 0.6631 4.2723
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.443868 0.434233 10.234 <2e-16 ***
currentDF[, TRAIT] -0.013742 0.076611 -0.179 0.8577
Age -0.003625 0.005870 -0.618 0.5372
Gendermale -0.238944 0.112701 -2.120 0.0346 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.062 on 435 degrees of freedom
Multiple R-squared: 0.01135, Adjusted R-squared: 0.004533
F-statistic: 1.665 on 3 and 435 DF, p-value: 0.1739
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_LN ' with ' VesselDensity_LN ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_LN
Trait/outcome.............: VesselDensity_LN
Effect size...............: -0.013742
Standard error............: 0.076611
Odds ratio (effect size)..: 0.986
Lower 95% CI..............: 0.849
Upper 95% CI..............: 1.146
T-value...................: -0.179371
P-value...................: 0.8577301
R^2.......................: 0.011351
Adjusted r^2..............: 0.004533
Sample size of AE DB......: 2388
Sample size of model......: 439
Missing data %............: 81.61642
Analysis of MCP1_LN.
- processing Macrophages_LN
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age Gendermale
4.923018 0.046963 -0.005936 0.195165
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.3536 -0.5289 0.0671 0.5818 2.1581
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.923018 0.285874 17.221 <2e-16 ***
currentDF[, TRAIT] 0.046963 0.018735 2.507 0.0125 *
Age -0.005936 0.004146 -1.432 0.1528
Gendermale 0.195165 0.079815 2.445 0.0148 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8323 on 538 degrees of freedom
Multiple R-squared: 0.02649, Adjusted R-squared: 0.02106
F-statistic: 4.88 on 3 and 538 DF, p-value: 0.002345
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_LN ' with ' Macrophages_LN ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_LN
Trait/outcome.............: Macrophages_LN
Effect size...............: 0.046963
Standard error............: 0.018735
Odds ratio (effect size)..: 1.048
Lower 95% CI..............: 1.01
Upper 95% CI..............: 1.087
T-value...................: 2.506756
P-value...................: 0.01247829
R^2.......................: 0.026489
Adjusted r^2..............: 0.02106
Sample size of AE DB......: 2388
Sample size of model......: 542
Missing data %............: 77.30318
- processing SMC_LN
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age Gendermale
5.24911 -0.11266 -0.01154 0.18294
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.15990 -0.51382 0.02868 0.55074 2.09503
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.24911 0.28416 18.472 < 2e-16 ***
currentDF[, TRAIT] -0.11266 0.02236 -5.039 6.39e-07 ***
Age -0.01154 0.00411 -2.809 0.00516 **
Gendermale 0.18294 0.07777 2.352 0.01902 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8187 on 544 degrees of freedom
Multiple R-squared: 0.06307, Adjusted R-squared: 0.0579
F-statistic: 12.21 on 3 and 544 DF, p-value: 9.694e-08
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_LN ' with ' SMC_LN ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_LN
Trait/outcome.............: SMC_LN
Effect size...............: -0.112658
Standard error............: 0.022359
Odds ratio (effect size)..: 0.893
Lower 95% CI..............: 0.855
Upper 95% CI..............: 0.933
T-value...................: -5.038517
P-value...................: 6.394823e-07
R^2.......................: 0.063065
Adjusted r^2..............: 0.057899
Sample size of AE DB......: 2388
Sample size of model......: 548
Missing data %............: 77.05193
- processing VesselDensity_LN
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age Gendermale
5.054615 -0.076923 -0.006881 0.239377
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.3180 -0.5236 0.0519 0.5880 2.0237
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.054615 0.307454 16.440 < 2e-16 ***
currentDF[, TRAIT] -0.076923 0.052418 -1.467 0.14283
Age -0.006881 0.004145 -1.660 0.09744 .
Gendermale 0.239377 0.079943 2.994 0.00288 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8369 on 536 degrees of freedom
Multiple R-squared: 0.02449, Adjusted R-squared: 0.01903
F-statistic: 4.485 on 3 and 536 DF, p-value: 0.004028
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_LN ' with ' VesselDensity_LN ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_LN
Trait/outcome.............: VesselDensity_LN
Effect size...............: -0.076923
Standard error............: 0.052418
Odds ratio (effect size)..: 0.926
Lower 95% CI..............: 0.836
Upper 95% CI..............: 1.026
T-value...................: -1.467485
P-value...................: 0.1428307
R^2.......................: 0.024489
Adjusted r^2..............: 0.019029
Sample size of AE DB......: 2388
Sample size of model......: 540
Missing data %............: 77.38693
Analysis of IL6_pg_ug_2015_LN.
- processing Macrophages_LN
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT]
-3.0631 0.1245
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-6.8417 -0.9325 -0.0252 0.8651 5.1402
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2.905299 0.333376 -8.715 < 2e-16 ***
currentDF[, TRAIT] 0.123333 0.024351 5.065 4.79e-07 ***
Age -0.002355 0.004747 -0.496 0.620
Gendermale 0.003428 0.094980 0.036 0.971
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.445 on 1103 degrees of freedom
Multiple R-squared: 0.02367, Adjusted R-squared: 0.02101
F-statistic: 8.913 on 3 and 1103 DF, p-value: 7.742e-06
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_LN ' with ' Macrophages_LN ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_pg_ug_2015_LN
Trait/outcome.............: Macrophages_LN
Effect size...............: 0.123333
Standard error............: 0.024351
Odds ratio (effect size)..: 1.131
Lower 95% CI..............: 1.079
Upper 95% CI..............: 1.187
T-value...................: 5.064799
P-value...................: 4.78904e-07
R^2.......................: 0.023669
Adjusted r^2..............: 0.021013
Sample size of AE DB......: 2388
Sample size of model......: 1107
Missing data %............: 53.64322
- processing SMC_LN
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age
-2.686652 -0.126982 -0.007997
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-6.6298 -0.9666 -0.0141 0.9192 5.5295
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2.675010 0.338711 -7.898 6.78e-15 ***
currentDF[, TRAIT] -0.127595 0.029628 -4.307 1.80e-05 ***
Age -0.008007 0.004792 -1.671 0.095 .
Gendermale -0.015907 0.095341 -0.167 0.868
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.455 on 1114 degrees of freedom
Multiple R-squared: 0.01734, Adjusted R-squared: 0.0147
F-statistic: 6.553 on 3 and 1114 DF, p-value: 0.0002158
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_LN ' with ' SMC_LN ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_pg_ug_2015_LN
Trait/outcome.............: SMC_LN
Effect size...............: -0.127595
Standard error............: 0.029628
Odds ratio (effect size)..: 0.88
Lower 95% CI..............: 0.831
Upper 95% CI..............: 0.933
T-value...................: -4.306616
P-value...................: 1.803788e-05
R^2.......................: 0.017342
Adjusted r^2..............: 0.014696
Sample size of AE DB......: 2388
Sample size of model......: 1118
Missing data %............: 53.18258
- processing VesselDensity_LN
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
-3.243
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-6.8345 -0.9328 -0.0131 0.9318 5.8822
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2.877457 0.368338 -7.812 1.42e-14 ***
currentDF[, TRAIT] -0.064379 0.053023 -1.214 0.225
Age -0.003647 0.005075 -0.719 0.473
Gendermale 0.007720 0.100992 0.076 0.939
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.473 on 999 degrees of freedom
Multiple R-squared: 0.00202, Adjusted R-squared: -0.0009774
F-statistic: 0.6739 on 3 and 999 DF, p-value: 0.5681
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_LN ' with ' VesselDensity_LN ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_pg_ug_2015_LN
Trait/outcome.............: VesselDensity_LN
Effect size...............: -0.064379
Standard error............: 0.053023
Odds ratio (effect size)..: 0.938
Lower 95% CI..............: 0.845
Upper 95% CI..............: 1.04
T-value...................: -1.214169
P-value...................: 0.22497
R^2.......................: 0.00202
Adjusted r^2..............: -0.000977
Sample size of AE DB......: 2388
Sample size of model......: 1003
Missing data %............: 57.99833
Analysis of IL6R_pg_ug_2015_LN.
- processing Macrophages_LN
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT]
-1.9115 0.1083
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-7.5941 -0.5052 0.1368 0.6836 3.4909
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.626395 0.264230 -6.155 1.05e-09 ***
currentDF[, TRAIT] 0.108785 0.018985 5.730 1.29e-08 ***
Age -0.003074 0.003757 -0.818 0.413
Gendermale -0.103988 0.075059 -1.385 0.166
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.137 on 1101 degrees of freedom
Multiple R-squared: 0.03136, Adjusted R-squared: 0.02872
F-statistic: 11.88 on 3 and 1101 DF, p-value: 1.164e-07
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_LN ' with ' Macrophages_LN ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6R_pg_ug_2015_LN
Trait/outcome.............: Macrophages_LN
Effect size...............: 0.108785
Standard error............: 0.018985
Odds ratio (effect size)..: 1.115
Lower 95% CI..............: 1.074
Upper 95% CI..............: 1.157
T-value...................: 5.73014
P-value...................: 1.294368e-08
R^2.......................: 0.031358
Adjusted r^2..............: 0.028718
Sample size of AE DB......: 2388
Sample size of model......: 1105
Missing data %............: 53.72697
- processing SMC_LN
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT]
-2.05286 0.05653
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-7.7438 -0.5087 0.1450 0.6970 3.5044
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.704077 0.268477 -6.347 3.19e-10 ***
currentDF[, TRAIT] 0.050503 0.023530 2.146 0.0321 *
Age -0.004681 0.003790 -1.235 0.2170
Gendermale -0.039226 0.075239 -0.521 0.6022
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.145 on 1113 degrees of freedom
Multiple R-squared: 0.006947, Adjusted R-squared: 0.00427
F-statistic: 2.595 on 3 and 1113 DF, p-value: 0.05119
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_LN ' with ' SMC_LN ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6R_pg_ug_2015_LN
Trait/outcome.............: SMC_LN
Effect size...............: 0.050503
Standard error............: 0.02353
Odds ratio (effect size)..: 1.052
Lower 95% CI..............: 1.004
Upper 95% CI..............: 1.101
T-value...................: 2.146317
P-value...................: 0.03206336
R^2.......................: 0.006947
Adjusted r^2..............: 0.00427
Sample size of AE DB......: 2388
Sample size of model......: 1117
Missing data %............: 53.22446
- processing VesselDensity_LN
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age
-1.878208 0.134082 -0.005983
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-7.7594 -0.4963 0.1434 0.6755 3.4833
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.833764 0.294155 -6.234 6.71e-10 ***
currentDF[, TRAIT] 0.134992 0.042081 3.208 0.00138 **
Age -0.005944 0.004021 -1.478 0.13964
Gendermale -0.070075 0.079798 -0.878 0.38007
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.159 on 995 degrees of freedom
Multiple R-squared: 0.01306, Adjusted R-squared: 0.01008
F-statistic: 4.389 on 3 and 995 DF, p-value: 0.004452
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_LN ' with ' VesselDensity_LN ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6R_pg_ug_2015_LN
Trait/outcome.............: VesselDensity_LN
Effect size...............: 0.134992
Standard error............: 0.042081
Odds ratio (effect size)..: 1.145
Lower 95% CI..............: 1.054
Upper 95% CI..............: 1.243
T-value...................: 3.207896
P-value...................: 0.001379794
R^2.......................: 0.01306
Adjusted r^2..............: 0.010085
Sample size of AE DB......: 2388
Sample size of model......: 999
Missing data %............: 58.16583
Analysis of MCP1_pg_ug_2015_LN.
- processing Macrophages_LN
Call:
lm(formula = currentDF[, PROTEIN] ~ Gender, data = currentDF)
Coefficients:
(Intercept) Gendermale
-1.3260 0.1275
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-5.8350 -0.7858 0.1422 0.8745 3.5979
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.4014251 0.2973607 -4.713 2.74e-06 ***
currentDF[, TRAIT] -0.0174719 0.0215143 -0.812 0.417
Age 0.0007108 0.0042430 0.168 0.867
Gendermale 0.1317384 0.0843699 1.561 0.119
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.314 on 1146 degrees of freedom
Multiple R-squared: 0.002625, Adjusted R-squared: 1.403e-05
F-statistic: 1.005 on 3 and 1146 DF, p-value: 0.3896
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_LN ' with ' Macrophages_LN ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_LN
Trait/outcome.............: Macrophages_LN
Effect size...............: -0.017472
Standard error............: 0.021514
Odds ratio (effect size)..: 0.983
Lower 95% CI..............: 0.942
Upper 95% CI..............: 1.025
T-value...................: -0.812106
P-value...................: 0.4168995
R^2.......................: 0.002625
Adjusted r^2..............: 1.4e-05
Sample size of AE DB......: 2388
Sample size of model......: 1150
Missing data %............: 51.84255
- processing SMC_LN
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT]
-1.24251 -0.08761
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-5.8716 -0.7610 0.1239 0.8561 3.6445
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.213542 0.298399 -4.067 5.09e-05 ***
currentDF[, TRAIT] -0.085281 0.026111 -3.266 0.00112 **
Age -0.001408 0.004225 -0.333 0.73903
Gendermale 0.098015 0.083613 1.172 0.24134
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.306 on 1158 degrees of freedom
Multiple R-squared: 0.01129, Adjusted R-squared: 0.008724
F-statistic: 4.406 on 3 and 1158 DF, p-value: 0.004325
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_LN ' with ' SMC_LN ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_LN
Trait/outcome.............: SMC_LN
Effect size...............: -0.085281
Standard error............: 0.026111
Odds ratio (effect size)..: 0.918
Lower 95% CI..............: 0.872
Upper 95% CI..............: 0.966
T-value...................: -3.266114
P-value...................: 0.001122294
R^2.......................: 0.011286
Adjusted r^2..............: 0.008724
Sample size of AE DB......: 2388
Sample size of model......: 1162
Missing data %............: 51.34003
- processing VesselDensity_LN
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale
-1.1213 -0.1529 0.1742
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-5.6832 -0.7657 0.1143 0.8364 3.6994
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.1691408 0.3260608 -3.586 0.000352 ***
currentDF[, TRAIT] -0.1528943 0.0467150 -3.273 0.001100 **
Age 0.0007009 0.0044823 0.156 0.875768
Gendermale 0.1739403 0.0887300 1.960 0.050224 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.325 on 1038 degrees of freedom
Multiple R-squared: 0.0135, Adjusted R-squared: 0.01065
F-statistic: 4.735 on 3 and 1038 DF, p-value: 0.002751
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_LN ' with ' VesselDensity_LN ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_LN
Trait/outcome.............: VesselDensity_LN
Effect size...............: -0.152894
Standard error............: 0.046715
Odds ratio (effect size)..: 0.858
Lower 95% CI..............: 0.783
Upper 95% CI..............: 0.941
T-value...................: -3.272916
P-value...................: 0.001099547
R^2.......................: 0.013501
Adjusted r^2..............: 0.01065
Sample size of AE DB......: 2388
Sample size of model......: 1042
Missing data %............: 56.36516 cat("Edit the column names...\n")Edit the column names...colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
"Beta", "s.e.m.",
"OR", "low95CI", "up95CI",
"T-value", "P-value", "r^2", "r^2_adj", "AE_N", "Model_N", "Perc_Miss")
cat("Correct the variable types...\n")Correct the variable types...GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)
# Save the data
cat("Writing results to Excel-file...\n")Writing results to Excel-file...### Univariate
library(openxlsx)
write.xlsx(GLM.results,
file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Con.Uni.Protein.PlaquePhenotypes.MODEL1.xlsx"),
row.names = FALSE, col.names = TRUE, sheetName = "Con.Uni.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")Removing intermediate files...rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)
Analysis of binary plaque traits as a function of serum/plaque IL6(R)/MCP1 levels.
GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN)) {
PROTEIN = TRAITS.PROTEIN[protein]
cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
for (trait in 1:length(TRAITS.BIN)) {
TRAIT = TRAITS.BIN[trait]
cat(paste0("\n- processing ",TRAIT,"\n\n"))
currentDF <- as.data.frame(AEDB.CEA %>%
dplyr::select(., PROTEIN, TRAIT, COVARIATES_M1) %>%
filter(complete.cases(.))) %>%
filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
# for debug
# print(DT::datatable(currentDF))
# print(nrow(currentDF))
# print(str(currentDF))
# print(class(currentDF[,TRAIT]))
### univariate
fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender,
data = currentDF, family = binomial(link = "logit"))
model_step <- stepAIC(fit, direction = "both", trace = FALSE)
print(model_step)
print(summary(fit))
GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
GLM.results = rbind(GLM.results, GLM.results.TEMP)
}
}
Analysis of IL6_LN.
- processing CalcificationPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ 1, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept)
0.3781
Degrees of Freedom: 454 Total (i.e. Null); 454 Residual
Null Deviance: 614.8
Residual Deviance: 614.8 AIC: 616.8
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.6505 -1.3011 0.9509 1.0358 1.1749
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.285658 0.854132 -0.334 0.738
currentDF[, PROTEIN] 0.116941 0.092238 1.268 0.205
Age 0.005461 0.011042 0.495 0.621
Gendermale -0.232339 0.217428 -1.069 0.285
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 614.79 on 454 degrees of freedom
Residual deviance: 611.48 on 451 degrees of freedom
AIC: 619.48
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_LN ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_LN
Trait/outcome.............: CalcificationPlaque
Effect size...............: 0.116941
Standard error............: 0.092238
Odds ratio (effect size)..: 1.124
Lower 95% CI..............: 0.938
Upper 95% CI..............: 1.347
Z-value...................: 1.267826
P-value...................: 0.20486
Hosmer and Lemeshow r^2...: 0.005383
Cox and Snell r^2.........: 0.007248
Nagelkerke's pseudo r^2...: 0.00978
Sample size of AE DB......: 2388
Sample size of model......: 455
Missing data %............: 80.9464
- processing CollagenPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ 1, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept)
1.329
Degrees of Freedom: 453 Total (i.e. Null); 453 Residual
Null Deviance: 465.8
Residual Deviance: 465.8 AIC: 467.8
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.9619 0.6186 0.6705 0.7060 0.8022
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 2.15828 1.05000 2.056 0.0398 *
currentDF[, PROTEIN] 0.04290 0.11173 0.384 0.7010
Age -0.01394 0.01353 -1.030 0.3030
Gendermale -0.07774 0.26262 -0.296 0.7672
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 465.77 on 453 degrees of freedom
Residual deviance: 464.42 on 450 degrees of freedom
AIC: 472.42
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_LN ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_LN
Trait/outcome.............: CollagenPlaque
Effect size...............: 0.042896
Standard error............: 0.111726
Odds ratio (effect size)..: 1.044
Lower 95% CI..............: 0.839
Upper 95% CI..............: 1.299
Z-value...................: 0.383945
P-value...................: 0.7010193
Hosmer and Lemeshow r^2...: 0.002889
Cox and Snell r^2.........: 0.00296
Nagelkerke's pseudo r^2...: 0.004613
Sample size of AE DB......: 2388
Sample size of model......: 454
Missing data %............: 80.98828
- processing Fat10Perc
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Gender, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) Gendermale
1.0468 0.5997
Degrees of Freedom: 454 Total (i.e. Null); 453 Residual
Null Deviance: 441.2
Residual Deviance: 435.7 AIC: 439.7
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.9435 0.5812 0.5948 0.6079 0.8147
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.782404 1.058021 0.739 0.4596
currentDF[, PROTEIN] 0.033995 0.113930 0.298 0.7654
Age 0.001833 0.013807 0.133 0.8944
Gendermale 0.607719 0.253718 2.395 0.0166 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 441.15 on 454 degrees of freedom
Residual deviance: 435.55 on 451 degrees of freedom
AIC: 443.55
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_LN ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_LN
Trait/outcome.............: Fat10Perc
Effect size...............: 0.033995
Standard error............: 0.11393
Odds ratio (effect size)..: 1.035
Lower 95% CI..............: 0.828
Upper 95% CI..............: 1.293
Z-value...................: 0.298383
P-value...................: 0.7654109
Hosmer and Lemeshow r^2...: 0.012706
Cox and Snell r^2.........: 0.012244
Nagelkerke's pseudo r^2...: 0.019724
Sample size of AE DB......: 2388
Sample size of model......: 455
Missing data %............: 80.9464
- processing IPH
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Gender, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) Gendermale
0.6696 0.7091
Degrees of Freedom: 454 Total (i.e. Null); 453 Residual
Null Deviance: 501
Residual Deviance: 492 AIC: 496
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.9621 0.5789 0.6611 0.7294 1.0949
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.19265 0.96939 -0.199 0.84247
currentDF[, PROTEIN] -0.07423 0.10339 -0.718 0.47278
Age 0.01758 0.01269 1.385 0.16591
Gendermale 0.68577 0.23472 2.922 0.00348 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 501.02 on 454 degrees of freedom
Residual deviance: 489.51 on 451 degrees of freedom
AIC: 497.51
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_LN ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_LN
Trait/outcome.............: IPH
Effect size...............: -0.074227
Standard error............: 0.103387
Odds ratio (effect size)..: 0.928
Lower 95% CI..............: 0.758
Upper 95% CI..............: 1.137
Z-value...................: -0.717958
P-value...................: 0.4727831
Hosmer and Lemeshow r^2...: 0.02297
Cox and Snell r^2.........: 0.024977
Nagelkerke's pseudo r^2...: 0.037417
Sample size of AE DB......: 2388
Sample size of model......: 455
Missing data %............: 80.9464
Analysis of MCP1_LN.
- processing CalcificationPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN],
family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN]
1.2754 -0.1911
Degrees of Freedom: 555 Total (i.e. Null); 554 Residual
Null Deviance: 749.7
Residual Deviance: 746.3 AIC: 750.3
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.5761 -1.3102 0.9272 1.0229 1.3026
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.38774 0.86096 0.450 0.6525
currentDF[, PROTEIN] -0.17455 0.10503 -1.662 0.0965 .
Age 0.01345 0.01008 1.334 0.1822
Gendermale -0.13028 0.19579 -0.665 0.5058
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 749.67 on 555 degrees of freedom
Residual deviance: 744.09 on 552 degrees of freedom
AIC: 752.09
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_LN ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_LN
Trait/outcome.............: CalcificationPlaque
Effect size...............: -0.174546
Standard error............: 0.105033
Odds ratio (effect size)..: 0.84
Lower 95% CI..............: 0.684
Upper 95% CI..............: 1.032
Z-value...................: -1.661819
P-value...................: 0.09654906
Hosmer and Lemeshow r^2...: 0.007442
Cox and Snell r^2.........: 0.009984
Nagelkerke's pseudo r^2...: 0.013486
Sample size of AE DB......: 2388
Sample size of model......: 556
Missing data %............: 76.71692
- processing CollagenPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN],
family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN]
4.7550 -0.6953
Degrees of Freedom: 553 Total (i.e. Null); 552 Residual
Null Deviance: 538
Residual Deviance: 512.7 AIC: 516.7
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.5263 0.3932 0.5643 0.6954 1.1490
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 5.87185 1.21117 4.848 1.25e-06 ***
currentDF[, PROTEIN] -0.70272 0.14786 -4.753 2.01e-06 ***
Age -0.01454 0.01314 -1.107 0.268
Gendermale -0.13269 0.25837 -0.514 0.608
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 537.98 on 553 degrees of freedom
Residual deviance: 511.18 on 550 degrees of freedom
AIC: 519.18
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_LN ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_LN
Trait/outcome.............: CollagenPlaque
Effect size...............: -0.702722
Standard error............: 0.147863
Odds ratio (effect size)..: 0.495
Lower 95% CI..............: 0.371
Upper 95% CI..............: 0.662
Z-value...................: -4.752516
P-value...................: 2.009007e-06
Hosmer and Lemeshow r^2...: 0.049807
Cox and Snell r^2.........: 0.047216
Nagelkerke's pseudo r^2...: 0.075992
Sample size of AE DB......: 2388
Sample size of model......: 554
Missing data %............: 76.80067
- processing Fat10Perc
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Gender, family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Gendermale
-2.3300 0.7694 0.5566
Degrees of Freedom: 555 Total (i.e. Null); 553 Residual
Null Deviance: 538.8
Residual Deviance: 496.6 AIC: 502.6
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.4336 0.3796 0.5147 0.6558 1.5060
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -2.698592 1.086903 -2.483 0.0130 *
currentDF[, PROTEIN] 0.772398 0.137592 5.614 1.98e-08 ***
Age 0.005304 0.012912 0.411 0.6813
Gendermale 0.553311 0.235382 2.351 0.0187 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 538.82 on 555 degrees of freedom
Residual deviance: 496.48 on 552 degrees of freedom
AIC: 504.48
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_LN ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_LN
Trait/outcome.............: Fat10Perc
Effect size...............: 0.772398
Standard error............: 0.137592
Odds ratio (effect size)..: 2.165
Lower 95% CI..............: 1.653
Upper 95% CI..............: 2.835
Z-value...................: 5.613695
P-value...................: 1.980514e-08
Hosmer and Lemeshow r^2...: 0.078584
Cox and Snell r^2.........: 0.073328
Nagelkerke's pseudo r^2...: 0.118161
Sample size of AE DB......: 2388
Sample size of model......: 556
Missing data %............: 76.71692
- processing IPH
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + Gender, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) Age Gendermale
-0.76646 0.02073 0.78990
Degrees of Freedom: 555 Total (i.e. Null); 553 Residual
Null Deviance: 611.8
Residual Deviance: 594.4 AIC: 600.4
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.0063 0.5568 0.6453 0.7253 1.1988
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -1.24578 0.98400 -1.266 0.205500
currentDF[, PROTEIN] 0.09778 0.12043 0.812 0.416817
Age 0.02140 0.01159 1.847 0.064728 .
Gendermale 0.77021 0.21170 3.638 0.000275 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 611.78 on 555 degrees of freedom
Residual deviance: 593.75 on 552 degrees of freedom
AIC: 601.75
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_LN ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_LN
Trait/outcome.............: IPH
Effect size...............: 0.097784
Standard error............: 0.12043
Odds ratio (effect size)..: 1.103
Lower 95% CI..............: 0.871
Upper 95% CI..............: 1.396
Z-value...................: 0.811956
P-value...................: 0.4168168
Hosmer and Lemeshow r^2...: 0.029473
Cox and Snell r^2.........: 0.03191
Nagelkerke's pseudo r^2...: 0.047824
Sample size of AE DB......: 2388
Sample size of model......: 556
Missing data %............: 76.71692
Analysis of IL6_pg_ug_2015_LN.
- processing CalcificationPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender, family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Age Gendermale
-1.07375 -0.07903 0.01345 -0.20223
Degrees of Freedom: 1133 Total (i.e. Null); 1130 Residual
Null Deviance: 1572
Residual Deviance: 1561 AIC: 1569
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.406 -1.152 -1.003 1.183 1.518
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -1.073750 0.475057 -2.260 0.0238 *
currentDF[, PROTEIN] -0.079029 0.041077 -1.924 0.0544 .
Age 0.013455 0.006507 2.068 0.0387 *
Gendermale -0.202231 0.129655 -1.560 0.1188
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1571.7 on 1133 degrees of freedom
Residual deviance: 1561.1 on 1130 degrees of freedom
AIC: 1569.1
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_LN ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_pg_ug_2015_LN
Trait/outcome.............: CalcificationPlaque
Effect size...............: -0.079029
Standard error............: 0.041077
Odds ratio (effect size)..: 0.924
Lower 95% CI..............: 0.853
Upper 95% CI..............: 1.001
Z-value...................: -1.923912
P-value...................: 0.05436566
Hosmer and Lemeshow r^2...: 0.006766
Cox and Snell r^2.........: 0.009334
Nagelkerke's pseudo r^2...: 0.012446
Sample size of AE DB......: 2388
Sample size of model......: 1134
Missing data %............: 52.51256
- processing CollagenPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN],
family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN]
0.7118 -0.1947
Degrees of Freedom: 1135 Total (i.e. Null); 1134 Residual
Null Deviance: 1172
Residual Deviance: 1156 AIC: 1160
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.1318 0.5468 0.6494 0.7162 1.0600
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.593805 0.574870 1.033 0.30163
currentDF[, PROTEIN] -0.193853 0.050658 -3.827 0.00013 ***
Age 0.002485 0.007934 0.313 0.75416
Gendermale -0.071069 0.160474 -0.443 0.65786
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1171.5 on 1135 degrees of freedom
Residual deviance: 1156.2 on 1132 degrees of freedom
AIC: 1164.2
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_LN ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_pg_ug_2015_LN
Trait/outcome.............: CollagenPlaque
Effect size...............: -0.193853
Standard error............: 0.050658
Odds ratio (effect size)..: 0.824
Lower 95% CI..............: 0.746
Upper 95% CI..............: 0.91
Z-value...................: -3.82672
P-value...................: 0.0001298622
Hosmer and Lemeshow r^2...: 0.013109
Cox and Snell r^2.........: 0.013428
Nagelkerke's pseudo r^2...: 0.020869
Sample size of AE DB......: 2388
Sample size of model......: 1136
Missing data %............: 52.42881
- processing Fat10Perc
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender, family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Age Gendermale
0.63818 0.34441 0.01408 0.86986
Degrees of Freedom: 1135 Total (i.e. Null); 1132 Residual
Null Deviance: 1320
Residual Deviance: 1231 AIC: 1239
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.2463 -1.0758 0.6255 0.7981 1.4856
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.638182 0.547585 1.165 0.244
currentDF[, PROTEIN] 0.344406 0.050528 6.816 9.35e-12 ***
Age 0.014081 0.007517 1.873 0.061 .
Gendermale 0.869856 0.144292 6.028 1.66e-09 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1319.7 on 1135 degrees of freedom
Residual deviance: 1231.1 on 1132 degrees of freedom
AIC: 1239.1
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_LN ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_pg_ug_2015_LN
Trait/outcome.............: Fat10Perc
Effect size...............: 0.344406
Standard error............: 0.050528
Odds ratio (effect size)..: 1.411
Lower 95% CI..............: 1.278
Upper 95% CI..............: 1.558
Z-value...................: 6.8162
P-value...................: 9.34802e-12
Hosmer and Lemeshow r^2...: 0.067178
Cox and Snell r^2.........: 0.075074
Nagelkerke's pseudo r^2...: 0.10927
Sample size of AE DB......: 2388
Sample size of model......: 1136
Missing data %............: 52.42881
- processing IPH
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Gender, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) Gendermale
0.02326 0.64534
Degrees of Freedom: 1134 Total (i.e. Null); 1133 Residual
Null Deviance: 1514
Residual Deviance: 1490 AIC: 1494
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.5574 -1.2367 0.8905 0.9307 1.2570
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.156562 0.486654 -0.322 0.748
currentDF[, PROTEIN] 0.046192 0.042332 1.091 0.275
Age 0.004824 0.006697 0.720 0.471
Gendermale 0.643408 0.131534 4.892 1e-06 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1513.8 on 1134 degrees of freedom
Residual deviance: 1488.0 on 1131 degrees of freedom
AIC: 1496
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_LN ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_pg_ug_2015_LN
Trait/outcome.............: IPH
Effect size...............: 0.046192
Standard error............: 0.042332
Odds ratio (effect size)..: 1.047
Lower 95% CI..............: 0.964
Upper 95% CI..............: 1.138
Z-value...................: 1.091195
P-value...................: 0.2751873
Hosmer and Lemeshow r^2...: 0.017028
Cox and Snell r^2.........: 0.022456
Nagelkerke's pseudo r^2...: 0.030489
Sample size of AE DB......: 2388
Sample size of model......: 1135
Missing data %............: 52.47069
Analysis of IL6R_pg_ug_2015_LN.
- processing CalcificationPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + Gender, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) Age Gendermale
-0.6992 0.0119 -0.2152
Degrees of Freedom: 1133 Total (i.e. Null); 1131 Residual
Null Deviance: 1572
Residual Deviance: 1566 AIC: 1572
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.346 -1.157 -1.038 1.190 1.363
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.655175 0.467724 -1.401 0.1613
currentDF[, PROTEIN] 0.027653 0.051832 0.534 0.5937
Age 0.012070 0.006531 1.848 0.0646 .
Gendermale -0.213914 0.129860 -1.647 0.0995 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1571.8 on 1133 degrees of freedom
Residual deviance: 1565.4 on 1130 degrees of freedom
AIC: 1573.4
Number of Fisher Scoring iterations: 3
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_LN ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6R_pg_ug_2015_LN
Trait/outcome.............: CalcificationPlaque
Effect size...............: 0.027653
Standard error............: 0.051832
Odds ratio (effect size)..: 1.028
Lower 95% CI..............: 0.929
Upper 95% CI..............: 1.138
Z-value...................: 0.533519
P-value...................: 0.5936745
Hosmer and Lemeshow r^2...: 0.00403
Cox and Snell r^2.........: 0.00557
Nagelkerke's pseudo r^2...: 0.007427
Sample size of AE DB......: 2388
Sample size of model......: 1134
Missing data %............: 52.51256
- processing CollagenPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ 1, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept)
1.307
Degrees of Freedom: 1135 Total (i.e. Null); 1135 Residual
Null Deviance: 1177
Residual Deviance: 1177 AIC: 1179
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.7994 0.6719 0.6873 0.6968 0.7493
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.125140 0.565385 1.990 0.0466 *
currentDF[, PROTEIN] 0.025097 0.062561 0.401 0.6883
Age 0.003103 0.007909 0.392 0.6948
Gendermale 0.029258 0.157523 0.186 0.8527
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1176.8 on 1135 degrees of freedom
Residual deviance: 1176.4 on 1132 degrees of freedom
AIC: 1184.4
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_LN ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6R_pg_ug_2015_LN
Trait/outcome.............: CollagenPlaque
Effect size...............: 0.025097
Standard error............: 0.062561
Odds ratio (effect size)..: 1.025
Lower 95% CI..............: 0.907
Upper 95% CI..............: 1.159
Z-value...................: 0.40116
P-value...................: 0.6883026
Hosmer and Lemeshow r^2...: 0.000281
Cox and Snell r^2.........: 0.000291
Nagelkerke's pseudo r^2...: 0.000451
Sample size of AE DB......: 2388
Sample size of model......: 1136
Missing data %............: 52.42881
- processing Fat10Perc
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender, family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Age Gendermale
-0.20010 0.12004 0.01319 0.82396
Degrees of Freedom: 1135 Total (i.e. Null); 1132 Residual
Null Deviance: 1324
Residual Deviance: 1284 AIC: 1292
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.8885 -1.2943 0.6856 0.7625 1.2713
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.200102 0.523490 -0.382 0.7023
currentDF[, PROTEIN] 0.120041 0.057677 2.081 0.0374 *
Age 0.013186 0.007375 1.788 0.0738 .
Gendermale 0.823957 0.141082 5.840 5.21e-09 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1323.7 on 1135 degrees of freedom
Residual deviance: 1283.6 on 1132 degrees of freedom
AIC: 1291.6
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_LN ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6R_pg_ug_2015_LN
Trait/outcome.............: Fat10Perc
Effect size...............: 0.120041
Standard error............: 0.057677
Odds ratio (effect size)..: 1.128
Lower 95% CI..............: 1.007
Upper 95% CI..............: 1.262
Z-value...................: 2.081254
P-value...................: 0.03741064
Hosmer and Lemeshow r^2...: 0.030306
Cox and Snell r^2.........: 0.034698
Nagelkerke's pseudo r^2...: 0.050422
Sample size of AE DB......: 2388
Sample size of model......: 1136
Missing data %............: 52.42881
- processing IPH
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Gender, family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Gendermale
0.2894 0.1198 0.6029
Degrees of Freedom: 1133 Total (i.e. Null); 1131 Residual
Null Deviance: 1515
Residual Deviance: 1489 AIC: 1495
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.6030 -1.2677 0.8841 0.9491 1.4022
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.105057 0.481377 0.218 0.8272
currentDF[, PROTEIN] 0.120979 0.053273 2.271 0.0232 *
Age 0.002723 0.006743 0.404 0.6863
Gendermale 0.602572 0.132109 4.561 5.09e-06 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1514.7 on 1133 degrees of freedom
Residual deviance: 1489.1 on 1130 degrees of freedom
AIC: 1497.1
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_LN ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6R_pg_ug_2015_LN
Trait/outcome.............: IPH
Effect size...............: 0.120979
Standard error............: 0.053273
Odds ratio (effect size)..: 1.129
Lower 95% CI..............: 1.017
Upper 95% CI..............: 1.253
Z-value...................: 2.27091
P-value...................: 0.02315245
Hosmer and Lemeshow r^2...: 0.016865
Cox and Snell r^2.........: 0.022275
Nagelkerke's pseudo r^2...: 0.030223
Sample size of AE DB......: 2388
Sample size of model......: 1134
Missing data %............: 52.51256
Analysis of MCP1_pg_ug_2015_LN.
- processing CalcificationPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender, family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Age Gendermale
-1.30828 -0.34278 0.01426 -0.19161
Degrees of Freedom: 1178 Total (i.e. Null); 1175 Residual
Null Deviance: 1634
Residual Deviance: 1572 AIC: 1580
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.9932 -1.1065 -0.7866 1.1439 1.7832
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -1.308282 0.466334 -2.805 0.00502 **
currentDF[, PROTEIN] -0.342779 0.048334 -7.092 1.32e-12 ***
Age 0.014260 0.006535 2.182 0.02910 *
Gendermale -0.191606 0.129659 -1.478 0.13947
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1633.9 on 1178 degrees of freedom
Residual deviance: 1572.1 on 1175 degrees of freedom
AIC: 1580.1
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_LN ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_LN
Trait/outcome.............: CalcificationPlaque
Effect size...............: -0.342779
Standard error............: 0.048334
Odds ratio (effect size)..: 0.71
Lower 95% CI..............: 0.646
Upper 95% CI..............: 0.78
Z-value...................: -7.09191
P-value...................: 1.322732e-12
Hosmer and Lemeshow r^2...: 0.037814
Cox and Snell r^2.........: 0.051054
Nagelkerke's pseudo r^2...: 0.068083
Sample size of AE DB......: 2388
Sample size of model......: 1179
Missing data %............: 50.62814
- processing CollagenPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN],
family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN]
1.106 -0.188
Degrees of Freedom: 1180 Total (i.e. Null); 1179 Residual
Null Deviance: 1217
Residual Deviance: 1205 AIC: 1209
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.2374 0.5500 0.6626 0.7178 0.9142
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.971527 0.550319 1.765 0.07750 .
currentDF[, PROTEIN] -0.187980 0.057268 -3.282 0.00103 **
Age 0.001926 0.007783 0.247 0.80459
Gendermale 0.004089 0.155715 0.026 0.97905
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1216.6 on 1180 degrees of freedom
Residual deviance: 1205.3 on 1177 degrees of freedom
AIC: 1213.3
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_LN ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_LN
Trait/outcome.............: CollagenPlaque
Effect size...............: -0.18798
Standard error............: 0.057268
Odds ratio (effect size)..: 0.829
Lower 95% CI..............: 0.741
Upper 95% CI..............: 0.927
Z-value...................: -3.282465
P-value...................: 0.001029038
Hosmer and Lemeshow r^2...: 0.00926
Cox and Snell r^2.........: 0.009494
Nagelkerke's pseudo r^2...: 0.014764
Sample size of AE DB......: 2388
Sample size of model......: 1181
Missing data %............: 50.54439
- processing Fat10Perc
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender, family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Age Gendermale
-0.17789 0.12296 0.01114 0.84851
Degrees of Freedom: 1180 Total (i.e. Null); 1177 Residual
Null Deviance: 1384
Residual Deviance: 1336 AIC: 1344
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.9359 -1.2862 0.6765 0.7691 1.2274
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.177889 0.507537 -0.350 0.7260
currentDF[, PROTEIN] 0.122961 0.050168 2.451 0.0142 *
Age 0.011138 0.007195 1.548 0.1216
Gendermale 0.848506 0.137295 6.180 6.4e-10 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1383.8 on 1180 degrees of freedom
Residual deviance: 1336.3 on 1177 degrees of freedom
AIC: 1344.3
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_LN ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_LN
Trait/outcome.............: Fat10Perc
Effect size...............: 0.122961
Standard error............: 0.050168
Odds ratio (effect size)..: 1.131
Lower 95% CI..............: 1.025
Upper 95% CI..............: 1.248
Z-value...................: 2.450975
P-value...................: 0.01424698
Hosmer and Lemeshow r^2...: 0.034374
Cox and Snell r^2.........: 0.039478
Nagelkerke's pseudo r^2...: 0.0572
Sample size of AE DB......: 2388
Sample size of model......: 1181
Missing data %............: 50.54439
- processing IPH
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Gender, family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Gendermale
-0.07727 -0.08375 0.62569
Degrees of Freedom: 1178 Total (i.e. Null); 1176 Residual
Null Deviance: 1576
Residual Deviance: 1550 AIC: 1556
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.6739 -1.2642 0.8917 0.9478 1.2963
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.274165 0.465648 -0.589 0.5560
currentDF[, PROTEIN] -0.083655 0.046529 -1.798 0.0722 .
Age 0.002880 0.006573 0.438 0.6613
Gendermale 0.624913 0.128837 4.850 1.23e-06 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1576.2 on 1178 degrees of freedom
Residual deviance: 1549.9 on 1175 degrees of freedom
AIC: 1557.9
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_LN ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_LN
Trait/outcome.............: IPH
Effect size...............: -0.083655
Standard error............: 0.046529
Odds ratio (effect size)..: 0.92
Lower 95% CI..............: 0.84
Upper 95% CI..............: 1.008
Z-value...................: -1.797891
P-value...................: 0.07219422
Hosmer and Lemeshow r^2...: 0.016644
Cox and Snell r^2.........: 0.022005
Nagelkerke's pseudo r^2...: 0.029844
Sample size of AE DB......: 2388
Sample size of model......: 1179
Missing data %............: 50.62814 cat("Edit the column names...\n")Edit the column names...colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
"Beta", "s.e.m.",
"OR", "low95CI", "up95CI",
"Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")
cat("Correct the variable types...\n")Correct the variable types...GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)
# Save the data
cat("Writing results to Excel-file...\n")Writing results to Excel-file...### Univariate
write.xlsx(GLM.results,
file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Uni.Protein.PlaquePhenotypes.MODEL1.xlsx"),
row.names = FALSE, col.names = TRUE, sheetName = "Bin.Uni.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")Removing intermediate files...rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)
Inverse-rank normalization
Here we use the inverse-rank normalized data - visually this is more normally distributed.
Analysis of continuous/quantitative plaque traits as a function of serum/plaque IL6(R)/MCP1 levels.
GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")Running linear regression...for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
PROTEIN = TRAITS.PROTEIN.RANK[protein]
cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
for (trait in 1:length(TRAITS.CON.RANK)) {
TRAIT = TRAITS.CON.RANK[trait]
cat(paste0("\n- processing ",TRAIT,"\n\n"))
currentDF <- as.data.frame(AEDB.CEA %>%
dplyr::select(., PROTEIN, TRAIT, COVARIATES_M1) %>%
filter(complete.cases(.))) %>%
filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
# for debug
# print(DT::datatable(currentDF))
# print(nrow(currentDF))
# print(str(currentDF))
### univariate
fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender, data = currentDF)
model_step <- stepAIC(fit, direction = "both", trace = FALSE)
print(model_step)
print(summary(fit))
GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
GLM.results.TEMP[1,] = GLM.CON(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
GLM.results = rbind(GLM.results, GLM.results.TEMP)
}
}
Analysis of IL6_rank.
- processing Macrophages_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Gender, data = currentDF)
Coefficients:
(Intercept) Gendermale
0.1147 -0.1457
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1.6651 -0.7037 -0.0163 0.6872 3.0540
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.265124 0.327047 0.811 0.418
currentDF[, TRAIT] -0.043457 0.038912 -1.117 0.265
Age -0.002260 0.004714 -0.479 0.632
Gendermale -0.139309 0.092751 -1.502 0.134
Residual standard error: 0.9589 on 523 degrees of freedom
Multiple R-squared: 0.007532, Adjusted R-squared: 0.001839
F-statistic: 1.323 on 3 and 523 DF, p-value: 0.266
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_rank ' with ' Macrophages_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_rank
Trait/outcome.............: Macrophages_rank
Effect size...............: -0.043457
Standard error............: 0.038912
Odds ratio (effect size)..: 0.957
Lower 95% CI..............: 0.887
Upper 95% CI..............: 1.033
T-value...................: -1.116809
P-value...................: 0.2645889
R^2.......................: 0.007532
Adjusted r^2..............: 0.001839
Sample size of AE DB......: 2388
Sample size of model......: 527
Missing data %............: 77.93132
- processing SMC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Gender, data = currentDF)
Coefficients:
(Intercept) Gendermale
0.1254 -0.1467
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1.66930 -0.69331 -0.00069 0.66680 2.97072
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.247782 0.339493 0.730 0.466
currentDF[, TRAIT] 0.023438 0.040868 0.573 0.567
Age -0.001916 0.004866 -0.394 0.694
Gendermale -0.140271 0.094251 -1.488 0.137
Residual standard error: 0.964 on 519 degrees of freedom
Multiple R-squared: 0.005833, Adjusted R-squared: 8.659e-05
F-statistic: 1.015 on 3 and 519 DF, p-value: 0.3856
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_rank ' with ' SMC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_rank
Trait/outcome.............: SMC_rank
Effect size...............: 0.023438
Standard error............: 0.040868
Odds ratio (effect size)..: 1.024
Lower 95% CI..............: 0.945
Upper 95% CI..............: 1.109
T-value...................: 0.573496
P-value...................: 0.5665574
R^2.......................: 0.005833
Adjusted r^2..............: 8.7e-05
Sample size of AE DB......: 2388
Sample size of model......: 523
Missing data %............: 78.09883
- processing VesselDensity_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Gender, data = currentDF)
Coefficients:
(Intercept) Gendermale
0.1137 -0.1357
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1.65204 -0.71205 -0.00561 0.69258 3.04629
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.335120 0.335382 0.999 0.318
currentDF[, TRAIT] -0.033064 0.052781 -0.626 0.531
Age -0.003204 0.004831 -0.663 0.508
Gendermale -0.135313 0.094253 -1.436 0.152
Residual standard error: 0.966 on 511 degrees of freedom
Multiple R-squared: 0.005607, Adjusted R-squared: -0.0002313
F-statistic: 0.9604 on 3 and 511 DF, p-value: 0.4112
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_rank ' with ' VesselDensity_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_rank
Trait/outcome.............: VesselDensity_rank
Effect size...............: -0.033064
Standard error............: 0.052781
Odds ratio (effect size)..: 0.967
Lower 95% CI..............: 0.872
Upper 95% CI..............: 1.073
T-value...................: -0.626443
P-value...................: 0.5313039
R^2.......................: 0.005607
Adjusted r^2..............: -0.000231
Sample size of AE DB......: 2388
Sample size of model......: 515
Missing data %............: 78.43384
Analysis of MCP1_rank.
- processing Macrophages_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age Gendermale
0.34006 0.10727 -0.00823 0.28612
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.66459 -0.66985 -0.00544 0.65388 2.97049
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.340061 0.327464 1.038 0.29950
currentDF[, TRAIT] 0.107266 0.038479 2.788 0.00549 **
Age -0.008230 0.004734 -1.738 0.08271 .
Gendermale 0.286120 0.091290 3.134 0.00181 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.98 on 560 degrees of freedom
Multiple R-squared: 0.03666, Adjusted R-squared: 0.0315
F-statistic: 7.105 on 3 and 560 DF, p-value: 0.0001085
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' Macrophages_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: Macrophages_rank
Effect size...............: 0.107266
Standard error............: 0.038479
Odds ratio (effect size)..: 1.113
Lower 95% CI..............: 1.032
Upper 95% CI..............: 1.2
T-value...................: 2.787615
P-value...................: 0.005490063
R^2.......................: 0.036664
Adjusted r^2..............: 0.031504
Sample size of AE DB......: 2388
Sample size of model......: 564
Missing data %............: 76.38191
- processing SMC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age Gendermale
0.74540 -0.18145 -0.01324 0.23454
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.67204 -0.64103 -0.03139 0.64140 2.69183
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.74540 0.33159 2.248 0.02497 *
currentDF[, TRAIT] -0.18145 0.03959 -4.583 5.65e-06 ***
Age -0.01324 0.00477 -2.775 0.00570 **
Gendermale 0.23454 0.09053 2.591 0.00982 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9621 on 556 degrees of freedom
Multiple R-squared: 0.05804, Adjusted R-squared: 0.05296
F-statistic: 11.42 on 3 and 556 DF, p-value: 2.82e-07
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' SMC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: SMC_rank
Effect size...............: -0.18145
Standard error............: 0.039588
Odds ratio (effect size)..: 0.834
Lower 95% CI..............: 0.772
Upper 95% CI..............: 0.901
T-value...................: -4.583453
P-value...................: 5.652515e-06
R^2.......................: 0.058039
Adjusted r^2..............: 0.052957
Sample size of AE DB......: 2388
Sample size of model......: 560
Missing data %............: 76.54941
- processing VesselDensity_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age Gendermale
0.39679 -0.09755 -0.00880 0.31637
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.69246 -0.65643 0.00031 0.65368 2.74036
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.39679 0.33186 1.196 0.23235
currentDF[, TRAIT] -0.09755 0.05111 -1.909 0.05680 .
Age -0.00880 0.00479 -1.837 0.06674 .
Gendermale 0.31637 0.09248 3.421 0.00067 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9842 on 549 degrees of freedom
Multiple R-squared: 0.03246, Adjusted R-squared: 0.02717
F-statistic: 6.139 on 3 and 549 DF, p-value: 0.0004131
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' VesselDensity_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: VesselDensity_rank
Effect size...............: -0.097554
Standard error............: 0.051106
Odds ratio (effect size)..: 0.907
Lower 95% CI..............: 0.821
Upper 95% CI..............: 1.003
T-value...................: -1.908837
P-value...................: 0.05680423
R^2.......................: 0.032459
Adjusted r^2..............: 0.027172
Sample size of AE DB......: 2388
Sample size of model......: 553
Missing data %............: 76.84255
Analysis of IL6_pg_ug_2015_rank.
- processing Macrophages_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT]
0.0008327 0.1298916
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.2750 -0.6882 0.0017 0.6410 3.6437
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.080646 0.226702 0.356 0.722
currentDF[, TRAIT] 0.128610 0.029926 4.298 1.88e-05 ***
Age -0.001259 0.003217 -0.391 0.696
Gendermale 0.009559 0.064377 0.148 0.882
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9916 on 1124 degrees of freedom
Multiple R-squared: 0.01684, Adjusted R-squared: 0.01421
F-statistic: 6.417 on 3 and 1124 DF, p-value: 0.0002611
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_rank ' with ' Macrophages_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_pg_ug_2015_rank
Trait/outcome.............: Macrophages_rank
Effect size...............: 0.12861
Standard error............: 0.029926
Odds ratio (effect size)..: 1.137
Lower 95% CI..............: 1.072
Upper 95% CI..............: 1.206
T-value...................: 4.297634
P-value...................: 1.875797e-05
R^2.......................: 0.016839
Adjusted r^2..............: 0.014215
Sample size of AE DB......: 2388
Sample size of model......: 1128
Missing data %............: 52.76382
- processing SMC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age
0.379605 -0.140378 -0.005529
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.0677 -0.6892 -0.0049 0.6794 3.1705
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.386998 0.231838 1.669 0.0953 .
currentDF[, TRAIT] -0.141045 0.031339 -4.501 7.48e-06 ***
Age -0.005538 0.003272 -1.692 0.0909 .
Gendermale -0.009751 0.064944 -0.150 0.8807
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9908 on 1120 degrees of freedom
Multiple R-squared: 0.01862, Adjusted R-squared: 0.01599
F-statistic: 7.084 on 3 and 1120 DF, p-value: 0.0001021
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_rank ' with ' SMC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_pg_ug_2015_rank
Trait/outcome.............: SMC_rank
Effect size...............: -0.141045
Standard error............: 0.031339
Odds ratio (effect size)..: 0.868
Lower 95% CI..............: 0.817
Upper 95% CI..............: 0.923
T-value...................: -4.5006
P-value...................: 7.483581e-06
R^2.......................: 0.018623
Adjusted r^2..............: 0.015994
Sample size of AE DB......: 2388
Sample size of model......: 1124
Missing data %............: 52.93132
- processing VesselDensity_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT]
-0.005939 -0.045061
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.2631 -0.6756 0.0016 0.6855 3.3859
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.113002 0.237668 0.475 0.635
currentDF[, TRAIT] -0.045329 0.031251 -1.450 0.147
Age -0.001943 0.003369 -0.577 0.564
Gendermale 0.020800 0.067259 0.309 0.757
Residual standard error: 1.002 on 1048 degrees of freedom
Multiple R-squared: 0.002385, Adjusted R-squared: -0.0004703
F-statistic: 0.8353 on 3 and 1048 DF, p-value: 0.4745
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_rank ' with ' VesselDensity_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_pg_ug_2015_rank
Trait/outcome.............: VesselDensity_rank
Effect size...............: -0.045329
Standard error............: 0.031251
Odds ratio (effect size)..: 0.956
Lower 95% CI..............: 0.899
Upper 95% CI..............: 1.016
T-value...................: -1.450483
P-value...................: 0.147223
R^2.......................: 0.002385
Adjusted r^2..............: -0.00047
Sample size of AE DB......: 2388
Sample size of model......: 1052
Missing data %............: 55.9464
Analysis of IL6R_pg_ug_2015_rank.
- processing Macrophages_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT]
0.006105 0.171717
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.1452 -0.6436 -0.0070 0.6466 3.3803
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.323725 0.226682 1.428 0.154
currentDF[, TRAIT] 0.171595 0.029416 5.833 7.09e-09 ***
Age -0.003819 0.003210 -1.190 0.234
Gendermale -0.078695 0.064098 -1.228 0.220
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9838 on 1124 degrees of freedom
Multiple R-squared: 0.03229, Adjusted R-squared: 0.02971
F-statistic: 12.5 on 3 and 1124 DF, p-value: 4.811e-08
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_rank ' with ' Macrophages_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6R_pg_ug_2015_rank
Trait/outcome.............: Macrophages_rank
Effect size...............: 0.171595
Standard error............: 0.029416
Odds ratio (effect size)..: 1.187
Lower 95% CI..............: 1.121
Upper 95% CI..............: 1.258
T-value...................: 5.833439
P-value...................: 7.092794e-09
R^2.......................: 0.03229
Adjusted r^2..............: 0.029707
Sample size of AE DB......: 2388
Sample size of model......: 1128
Missing data %............: 52.76382
- processing SMC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT]
0.003564 0.085455
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.2788 -0.6560 0.0049 0.6817 3.3960
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.279731 0.234166 1.195 0.2325
currentDF[, TRAIT] 0.077483 0.031462 2.463 0.0139 *
Age -0.003775 0.003299 -1.145 0.2526
Gendermale -0.023816 0.065312 -0.365 0.7154
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9939 on 1120 degrees of freedom
Multiple R-squared: 0.008145, Adjusted R-squared: 0.005488
F-statistic: 3.066 on 3 and 1120 DF, p-value: 0.0272
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_rank ' with ' SMC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6R_pg_ug_2015_rank
Trait/outcome.............: SMC_rank
Effect size...............: 0.077483
Standard error............: 0.031462
Odds ratio (effect size)..: 1.081
Lower 95% CI..............: 1.016
Upper 95% CI..............: 1.149
T-value...................: 2.462754
P-value...................: 0.0139371
R^2.......................: 0.008145
Adjusted r^2..............: 0.005488
Sample size of AE DB......: 2388
Sample size of model......: 1124
Missing data %............: 52.93132
- processing VesselDensity_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age
0.363777 0.108548 -0.005145
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.3980 -0.6565 -0.0131 0.6619 3.3784
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.40529 0.23903 1.696 0.09027 .
currentDF[, TRAIT] 0.10889 0.03133 3.476 0.00053 ***
Age -0.00514 0.00338 -1.521 0.12864
Gendermale -0.05983 0.06724 -0.890 0.37373
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9985 on 1047 degrees of freedom
Multiple R-squared: 0.01437, Adjusted R-squared: 0.01154
F-statistic: 5.087 on 3 and 1047 DF, p-value: 0.001687
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_rank ' with ' VesselDensity_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6R_pg_ug_2015_rank
Trait/outcome.............: VesselDensity_rank
Effect size...............: 0.108891
Standard error............: 0.03133
Odds ratio (effect size)..: 1.115
Lower 95% CI..............: 1.049
Upper 95% CI..............: 1.186
T-value...................: 3.475655
P-value...................: 0.0005304978
R^2.......................: 0.014366
Adjusted r^2..............: 0.011542
Sample size of AE DB......: 2388
Sample size of model......: 1051
Missing data %............: 55.98828
Analysis of MCP1_pg_ug_2015_rank.
- processing Macrophages_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale
-0.08851 -0.04407 0.10833
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.4091 -0.6867 0.0027 0.6638 3.3363
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.0521469 0.2245097 -0.232 0.8164
currentDF[, TRAIT] -0.0445154 0.0293223 -1.518 0.1292
Age -0.0005313 0.0031888 -0.167 0.8677
Gendermale 0.1084819 0.0633932 1.711 0.0873 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9994 on 1168 degrees of freedom
Multiple R-squared: 0.004169, Adjusted R-squared: 0.001611
F-statistic: 1.63 on 3 and 1168 DF, p-value: 0.1807
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' Macrophages_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: Macrophages_rank
Effect size...............: -0.044515
Standard error............: 0.029322
Odds ratio (effect size)..: 0.956
Lower 95% CI..............: 0.903
Upper 95% CI..............: 1.013
T-value...................: -1.518139
P-value...................: 0.1292499
R^2.......................: 0.004169
Adjusted r^2..............: 0.001611
Sample size of AE DB......: 2388
Sample size of model......: 1172
Missing data %............: 50.92127
- processing SMC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT]
-0.01583 -0.11198
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.4371 -0.6552 -0.0098 0.6354 3.3670
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.103027 0.228379 0.451 0.651986
currentDF[, TRAIT] -0.111746 0.030611 -3.651 0.000273 ***
Age -0.002390 0.003226 -0.741 0.458971
Gendermale 0.065262 0.063722 1.024 0.305970
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9951 on 1164 degrees of freedom
Multiple R-squared: 0.01332, Adjusted R-squared: 0.01078
F-statistic: 5.238 on 3 and 1164 DF, p-value: 0.001359
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' SMC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: SMC_rank
Effect size...............: -0.111746
Standard error............: 0.030611
Odds ratio (effect size)..: 0.894
Lower 95% CI..............: 0.842
Upper 95% CI..............: 0.95
T-value...................: -3.650566
P-value...................: 0.0002732734
R^2.......................: 0.013321
Adjusted r^2..............: 0.010778
Sample size of AE DB......: 2388
Sample size of model......: 1168
Missing data %............: 51.08878
- processing VesselDensity_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale
-0.1167 -0.1300 0.1283
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.2795 -0.6726 0.0001 0.6322 3.4098
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.1460528 0.2332909 -0.626 0.5314
currentDF[, TRAIT] -0.1299743 0.0305087 -4.260 2.22e-05 ***
Age 0.0004283 0.0033099 0.129 0.8971
Gendermale 0.1282621 0.0656979 1.952 0.0512 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.002 on 1090 degrees of freedom
Multiple R-squared: 0.01952, Adjusted R-squared: 0.01682
F-statistic: 7.234 on 3 and 1090 DF, p-value: 8.293e-05
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' VesselDensity_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: VesselDensity_rank
Effect size...............: -0.129974
Standard error............: 0.030509
Odds ratio (effect size)..: 0.878
Lower 95% CI..............: 0.827
Upper 95% CI..............: 0.932
T-value...................: -4.260235
P-value...................: 2.218457e-05
R^2.......................: 0.019522
Adjusted r^2..............: 0.016823
Sample size of AE DB......: 2388
Sample size of model......: 1094
Missing data %............: 54.1876 cat("Edit the column names...\n")Edit the column names...colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
"Beta", "s.e.m.",
"OR", "low95CI", "up95CI",
"T-value", "P-value", "r^2", "r^2_adj", "AE_N", "Model_N", "Perc_Miss")
cat("Correct the variable types...\n")Correct the variable types...GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)
# Save the data
cat("Writing results to Excel-file...\n")Writing results to Excel-file...### Univariate
library(openxlsx)
write.xlsx(GLM.results,
file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Con.Uni.Protein.PlaquePhenotypes.RANK.MODEL1.xlsx"),
row.names = FALSE, col.names = TRUE, sheetName = "Con.Uni.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")Removing intermediate files...rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)
Analysis of binary plaque traits as a function of serum/plaque IL6(R)/MCP1 levels.
GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
PROTEIN = TRAITS.PROTEIN.RANK[protein]
cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
for (trait in 1:length(TRAITS.BIN)) {
TRAIT = TRAITS.BIN[trait]
cat(paste0("\n- processing ",TRAIT,"\n\n"))
currentDF <- as.data.frame(AEDB.CEA %>%
dplyr::select(., PROTEIN, TRAIT, COVARIATES_M1) %>%
filter(complete.cases(.))) %>%
filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
# for debug
# print(DT::datatable(currentDF))
# print(nrow(currentDF))
# print(str(currentDF))
# print(class(currentDF[,TRAIT]))
### univariate
fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender,
data = currentDF, family = binomial(link = "logit"))
model_step <- stepAIC(fit, direction = "both", trace = FALSE)
print(model_step)
print(summary(fit))
GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
GLM.results = rbind(GLM.results, GLM.results.TEMP)
}
}
Analysis of IL6_rank.
- processing CalcificationPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN],
family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN]
0.3364 0.1700
Degrees of Freedom: 527 Total (i.e. Null); 526 Residual
Null Deviance: 717.2
Residual Deviance: 713.8 AIC: 717.8
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.6055 -1.2824 0.9442 1.0453 1.2408
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.39815 0.69417 -0.574 0.5663
currentDF[, PROTEIN] 0.16861 0.09329 1.807 0.0707 .
Age 0.01255 0.01003 1.251 0.2108
Gendermale -0.15185 0.19853 -0.765 0.4443
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 717.23 on 527 degrees of freedom
Residual deviance: 711.68 on 524 degrees of freedom
AIC: 719.68
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_rank ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_rank
Trait/outcome.............: CalcificationPlaque
Effect size...............: 0.168614
Standard error............: 0.093291
Odds ratio (effect size)..: 1.184
Lower 95% CI..............: 0.986
Upper 95% CI..............: 1.421
Z-value...................: 1.807395
P-value...................: 0.07070067
Hosmer and Lemeshow r^2...: 0.007736
Cox and Snell r^2.........: 0.010453
Nagelkerke's pseudo r^2...: 0.01407
Sample size of AE DB......: 2388
Sample size of model......: 528
Missing data %............: 77.88945
- processing CollagenPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ 1, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept)
1.439
Degrees of Freedom: 526 Total (i.e. Null); 526 Residual
Null Deviance: 515
Residual Deviance: 515 AIC: 517
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.0039 0.5622 0.6281 0.6781 0.8143
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 2.50639 0.90034 2.784 0.00537 **
currentDF[, PROTEIN] -0.15233 0.11589 -1.314 0.18871
Age -0.01370 0.01281 -1.069 0.28490
Gendermale -0.18207 0.25283 -0.720 0.47146
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 514.99 on 526 degrees of freedom
Residual deviance: 511.76 on 523 degrees of freedom
AIC: 519.76
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_rank ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_rank
Trait/outcome.............: CollagenPlaque
Effect size...............: -0.152332
Standard error............: 0.115895
Odds ratio (effect size)..: 0.859
Lower 95% CI..............: 0.684
Upper 95% CI..............: 1.078
Z-value...................: -1.314401
P-value...................: 0.1887114
Hosmer and Lemeshow r^2...: 0.006272
Cox and Snell r^2.........: 0.00611
Nagelkerke's pseudo r^2...: 0.009798
Sample size of AE DB......: 2388
Sample size of model......: 527
Missing data %............: 77.93132
- processing Fat10Perc
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Gender, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) Gendermale
0.8473 0.8007
Degrees of Freedom: 527 Total (i.e. Null); 526 Residual
Null Deviance: 529.5
Residual Deviance: 517.4 AIC: 521.4
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.0323 0.5426 0.5923 0.6460 0.9468
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.633935 0.848167 0.747 0.454812
currentDF[, PROTEIN] 0.142074 0.114411 1.242 0.214314
Age 0.002994 0.012321 0.243 0.807985
Gendermale 0.822167 0.227638 3.612 0.000304 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 529.53 on 527 degrees of freedom
Residual deviance: 515.77 on 524 degrees of freedom
AIC: 523.77
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_rank ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_rank
Trait/outcome.............: Fat10Perc
Effect size...............: 0.142074
Standard error............: 0.114411
Odds ratio (effect size)..: 1.153
Lower 95% CI..............: 0.921
Upper 95% CI..............: 1.442
Z-value...................: 1.24179
P-value...................: 0.214314
Hosmer and Lemeshow r^2...: 0.025995
Cox and Snell r^2.........: 0.025733
Nagelkerke's pseudo r^2...: 0.040641
Sample size of AE DB......: 2388
Sample size of model......: 528
Missing data %............: 77.88945
- processing IPH
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + Gender, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) Age Gendermale
-1.53026 0.03187 0.77077
Degrees of Freedom: 527 Total (i.e. Null); 525 Residual
Null Deviance: 589.4
Residual Deviance: 569.3 AIC: 575.3
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.0272 0.5126 0.6441 0.7617 1.3318
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -1.531376 0.788651 -1.942 0.052165 .
currentDF[, PROTEIN] 0.004591 0.106067 0.043 0.965479
Age 0.031879 0.011544 2.761 0.005754 **
Gendermale 0.771453 0.215860 3.574 0.000352 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 589.39 on 527 degrees of freedom
Residual deviance: 569.29 on 524 degrees of freedom
AIC: 577.29
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_rank ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_rank
Trait/outcome.............: IPH
Effect size...............: 0.004591
Standard error............: 0.106067
Odds ratio (effect size)..: 1.005
Lower 95% CI..............: 0.816
Upper 95% CI..............: 1.237
Z-value...................: 0.043279
P-value...................: 0.9654788
Hosmer and Lemeshow r^2...: 0.034097
Cox and Snell r^2.........: 0.037347
Nagelkerke's pseudo r^2...: 0.055534
Sample size of AE DB......: 2388
Sample size of model......: 528
Missing data %............: 77.88945
Analysis of MCP1_rank.
- processing CalcificationPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Age, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) Age
-0.66894 0.01552
Degrees of Freedom: 564 Total (i.e. Null); 563 Residual
Null Deviance: 763.6
Residual Deviance: 761.1 AIC: 765.1
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.5354 -1.3063 0.9435 1.0328 1.2735
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.520510 0.681656 -0.764 0.445
currentDF[, PROTEIN] -0.084572 0.087486 -0.967 0.334
Age 0.015036 0.009908 1.518 0.129
Gendermale -0.159870 0.193286 -0.827 0.408
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 763.63 on 564 degrees of freedom
Residual deviance: 759.27 on 561 degrees of freedom
AIC: 767.27
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: CalcificationPlaque
Effect size...............: -0.084572
Standard error............: 0.087486
Odds ratio (effect size)..: 0.919
Lower 95% CI..............: 0.774
Upper 95% CI..............: 1.091
Z-value...................: -0.966696
P-value...................: 0.3336962
Hosmer and Lemeshow r^2...: 0.005706
Cox and Snell r^2.........: 0.007683
Nagelkerke's pseudo r^2...: 0.010366
Sample size of AE DB......: 2388
Sample size of model......: 565
Missing data %............: 76.34003
- processing CollagenPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN],
family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN]
1.5378 -0.5379
Degrees of Freedom: 562 Total (i.e. Null); 561 Residual
Null Deviance: 547.6
Residual Deviance: 524.3 AIC: 528.3
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.4625 0.4102 0.5643 0.6848 1.1638
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 2.84690 0.91211 3.121 0.0018 **
currentDF[, PROTEIN] -0.54841 0.11723 -4.678 2.9e-06 ***
Age -0.01803 0.01302 -1.384 0.1663
Gendermale -0.11699 0.25442 -0.460 0.6456
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 547.57 on 562 degrees of freedom
Residual deviance: 522.07 on 559 degrees of freedom
AIC: 530.07
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: CollagenPlaque
Effect size...............: -0.548413
Standard error............: 0.117233
Odds ratio (effect size)..: 0.578
Lower 95% CI..............: 0.459
Upper 95% CI..............: 0.727
Z-value...................: -4.677968
P-value...................: 2.897316e-06
Hosmer and Lemeshow r^2...: 0.046582
Cox and Snell r^2.........: 0.044294
Nagelkerke's pseudo r^2...: 0.071224
Sample size of AE DB......: 2388
Sample size of model......: 563
Missing data %............: 76.42379
- processing Fat10Perc
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Gender, family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Gendermale
1.1894 0.6807 0.5650
Degrees of Freedom: 564 Total (i.e. Null); 562 Residual
Null Deviance: 554.2
Residual Deviance: 509.3 AIC: 515.3
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.4929 0.3605 0.5225 0.6691 1.3725
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.85428 0.86465 0.988 0.3231
currentDF[, PROTEIN] 0.68303 0.12124 5.634 1.76e-08 ***
Age 0.00499 0.01257 0.397 0.6914
Gendermale 0.56378 0.23128 2.438 0.0148 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 554.19 on 564 degrees of freedom
Residual deviance: 509.11 on 561 degrees of freedom
AIC: 517.11
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: Fat10Perc
Effect size...............: 0.68303
Standard error............: 0.12124
Odds ratio (effect size)..: 1.98
Lower 95% CI..............: 1.561
Upper 95% CI..............: 2.511
Z-value...................: 5.633721
P-value...................: 1.763624e-08
Hosmer and Lemeshow r^2...: 0.081344
Cox and Snell r^2.........: 0.076687
Nagelkerke's pseudo r^2...: 0.122697
Sample size of AE DB......: 2388
Sample size of model......: 565
Missing data %............: 76.34003
- processing IPH
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + Gender, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) Age Gendermale
-1.02619 0.02446 0.78389
Degrees of Freedom: 564 Total (i.e. Null); 562 Residual
Null Deviance: 625.9
Residual Deviance: 607.1 AIC: 613.1
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.0414 0.5339 0.6471 0.7406 1.2555
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -1.06199 0.77564 -1.369 0.170946
currentDF[, PROTEIN] 0.10109 0.10144 0.997 0.318965
Age 0.02532 0.01139 2.224 0.026177 *
Gendermale 0.75630 0.20942 3.611 0.000304 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 625.93 on 564 degrees of freedom
Residual deviance: 606.14 on 561 degrees of freedom
AIC: 614.14
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: IPH
Effect size...............: 0.101091
Standard error............: 0.101437
Odds ratio (effect size)..: 1.106
Lower 95% CI..............: 0.907
Upper 95% CI..............: 1.35
Z-value...................: 0.996588
P-value...................: 0.3189645
Hosmer and Lemeshow r^2...: 0.031618
Cox and Snell r^2.........: 0.034421
Nagelkerke's pseudo r^2...: 0.051395
Sample size of AE DB......: 2388
Sample size of model......: 565
Missing data %............: 76.34003
Analysis of IL6_pg_ug_2015_rank.
- processing CalcificationPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender, family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Age Gendermale
-0.82326 -0.11365 0.01351 -0.20124
Degrees of Freedom: 1133 Total (i.e. Null); 1130 Residual
Null Deviance: 1572
Residual Deviance: 1561 AIC: 1569
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.405 -1.151 -1.013 1.182 1.494
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.823262 0.458227 -1.797 0.0724 .
currentDF[, PROTEIN] -0.113647 0.059989 -1.894 0.0582 .
Age 0.013512 0.006506 2.077 0.0378 *
Gendermale -0.201235 0.129656 -1.552 0.1206
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1571.7 on 1133 degrees of freedom
Residual deviance: 1561.2 on 1130 degrees of freedom
AIC: 1569.2
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_rank ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_pg_ug_2015_rank
Trait/outcome.............: CalcificationPlaque
Effect size...............: -0.113647
Standard error............: 0.059989
Odds ratio (effect size)..: 0.893
Lower 95% CI..............: 0.794
Upper 95% CI..............: 1.004
Z-value...................: -1.89445
P-value...................: 0.05816533
Hosmer and Lemeshow r^2...: 0.006689
Cox and Snell r^2.........: 0.009228
Nagelkerke's pseudo r^2...: 0.012306
Sample size of AE DB......: 2388
Sample size of model......: 1134
Missing data %............: 52.51256
- processing CollagenPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN],
family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN]
1.3407 -0.2859
Degrees of Freedom: 1135 Total (i.e. Null); 1134 Residual
Null Deviance: 1172
Residual Deviance: 1156 AIC: 1160
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.0925 0.5434 0.6477 0.7187 0.9893
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.209758 0.559055 2.164 0.030469 *
currentDF[, PROTEIN] -0.284656 0.074277 -3.832 0.000127 ***
Age 0.002611 0.007932 0.329 0.742003
Gendermale -0.068954 0.160475 -0.430 0.667424
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1171.5 on 1135 degrees of freedom
Residual deviance: 1156.1 on 1132 degrees of freedom
AIC: 1164.1
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_rank ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_pg_ug_2015_rank
Trait/outcome.............: CollagenPlaque
Effect size...............: -0.284656
Standard error............: 0.074277
Odds ratio (effect size)..: 0.752
Lower 95% CI..............: 0.65
Upper 95% CI..............: 0.87
Z-value...................: -3.832368
P-value...................: 0.0001269155
Hosmer and Lemeshow r^2...: 0.013161
Cox and Snell r^2.........: 0.013481
Nagelkerke's pseudo r^2...: 0.020951
Sample size of AE DB......: 2388
Sample size of model......: 1136
Missing data %............: 52.42881
- processing Fat10Perc
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender, family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Age Gendermale
-0.45642 0.49681 0.01386 0.86551
Degrees of Freedom: 1135 Total (i.e. Null); 1132 Residual
Null Deviance: 1320
Residual Deviance: 1232 AIC: 1240
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.2318 -1.0860 0.6253 0.8001 1.4676
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.456424 0.525551 -0.868 0.3851
currentDF[, PROTEIN] 0.496810 0.072834 6.821 9.03e-12 ***
Age 0.013863 0.007517 1.844 0.0651 .
Gendermale 0.865510 0.144282 5.999 1.99e-09 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1319.7 on 1135 degrees of freedom
Residual deviance: 1231.7 on 1132 degrees of freedom
AIC: 1239.7
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_rank ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_pg_ug_2015_rank
Trait/outcome.............: Fat10Perc
Effect size...............: 0.49681
Standard error............: 0.072834
Odds ratio (effect size)..: 1.643
Lower 95% CI..............: 1.425
Upper 95% CI..............: 1.896
Z-value...................: 6.821123
P-value...................: 9.033127e-12
Hosmer and Lemeshow r^2...: 0.066674
Cox and Snell r^2.........: 0.074533
Nagelkerke's pseudo r^2...: 0.108482
Sample size of AE DB......: 2388
Sample size of model......: 1136
Missing data %............: 52.42881
- processing IPH
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Gender, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) Gendermale
0.02326 0.64534
Degrees of Freedom: 1134 Total (i.e. Null); 1133 Residual
Null Deviance: 1514
Residual Deviance: 1490 AIC: 1494
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.5453 -1.2340 0.8904 0.9307 1.2566
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.302686 0.470800 -0.643 0.520
currentDF[, PROTEIN] 0.065082 0.061907 1.051 0.293
Age 0.004786 0.006696 0.715 0.475
Gendermale 0.642790 0.131532 4.887 1.02e-06 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1513.8 on 1134 degrees of freedom
Residual deviance: 1488.1 on 1131 degrees of freedom
AIC: 1496.1
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_rank ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_pg_ug_2015_rank
Trait/outcome.............: IPH
Effect size...............: 0.065082
Standard error............: 0.061907
Odds ratio (effect size)..: 1.067
Lower 95% CI..............: 0.945
Upper 95% CI..............: 1.205
Z-value...................: 1.051285
P-value...................: 0.2931276
Hosmer and Lemeshow r^2...: 0.016972
Cox and Snell r^2.........: 0.022382
Nagelkerke's pseudo r^2...: 0.030389
Sample size of AE DB......: 2388
Sample size of model......: 1135
Missing data %............: 52.47069
Analysis of IL6R_pg_ug_2015_rank.
- processing CalcificationPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + Gender, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) Age Gendermale
-0.70308 0.01195 -0.21761
Degrees of Freedom: 1134 Total (i.e. Null); 1132 Residual
Null Deviance: 1573
Residual Deviance: 1567 AIC: 1573
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.351 -1.156 -1.036 1.190 1.372
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.722969 0.461171 -1.568 0.1170
currentDF[, PROTEIN] 0.050413 0.059798 0.843 0.3992
Age 0.012220 0.006533 1.871 0.0614 .
Gendermale -0.215830 0.129855 -1.662 0.0965 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1573.1 on 1134 degrees of freedom
Residual deviance: 1566.3 on 1131 degrees of freedom
AIC: 1574.3
Number of Fisher Scoring iterations: 3
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_rank ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6R_pg_ug_2015_rank
Trait/outcome.............: CalcificationPlaque
Effect size...............: 0.050413
Standard error............: 0.059798
Odds ratio (effect size)..: 1.052
Lower 95% CI..............: 0.935
Upper 95% CI..............: 1.182
Z-value...................: 0.843058
P-value...................: 0.3991959
Hosmer and Lemeshow r^2...: 0.004357
Cox and Snell r^2.........: 0.006021
Nagelkerke's pseudo r^2...: 0.008028
Sample size of AE DB......: 2388
Sample size of model......: 1135
Missing data %............: 52.47069
- processing CollagenPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ 1, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept)
1.303
Degrees of Freedom: 1136 Total (i.e. Null); 1136 Residual
Null Deviance: 1180
Residual Deviance: 1180 AIC: 1182
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.8412 0.6608 0.6871 0.7021 0.7501
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.055209 0.556645 1.896 0.058 .
currentDF[, PROTEIN] 0.054395 0.072780 0.747 0.455
Age 0.003366 0.007898 0.426 0.670
Gendermale 0.024221 0.157433 0.154 0.878
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1179.9 on 1136 degrees of freedom
Residual deviance: 1179.1 on 1133 degrees of freedom
AIC: 1187.1
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_rank ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6R_pg_ug_2015_rank
Trait/outcome.............: CollagenPlaque
Effect size...............: 0.054395
Standard error............: 0.07278
Odds ratio (effect size)..: 1.056
Lower 95% CI..............: 0.916
Upper 95% CI..............: 1.218
Z-value...................: 0.74739
P-value...................: 0.4548283
Hosmer and Lemeshow r^2...: 0.000619
Cox and Snell r^2.........: 0.000642
Nagelkerke's pseudo r^2...: 0.000995
Sample size of AE DB......: 2388
Sample size of model......: 1137
Missing data %............: 52.38694
- processing Fat10Perc
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender, family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Age Gendermale
-0.44254 0.13586 0.01312 0.82461
Degrees of Freedom: 1136 Total (i.e. Null); 1133 Residual
Null Deviance: 1324
Residual Deviance: 1284 AIC: 1292
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.9008 -1.2841 0.6888 0.7635 1.2184
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.442537 0.517161 -0.856 0.3922
currentDF[, PROTEIN] 0.135860 0.068675 1.978 0.0479 *
Age 0.013117 0.007375 1.779 0.0753 .
Gendermale 0.824605 0.141034 5.847 5.01e-09 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1324.4 on 1136 degrees of freedom
Residual deviance: 1284.5 on 1133 degrees of freedom
AIC: 1292.5
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_rank ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6R_pg_ug_2015_rank
Trait/outcome.............: Fat10Perc
Effect size...............: 0.13586
Standard error............: 0.068675
Odds ratio (effect size)..: 1.146
Lower 95% CI..............: 1.001
Upper 95% CI..............: 1.311
Z-value...................: 1.97829
P-value...................: 0.04789604
Hosmer and Lemeshow r^2...: 0.030128
Cox and Snell r^2.........: 0.034484
Nagelkerke's pseudo r^2...: 0.050122
Sample size of AE DB......: 2388
Sample size of model......: 1137
Missing data %............: 52.38694
- processing IPH
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Gender, family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Gendermale
0.04247 0.15197 0.60545
Degrees of Freedom: 1134 Total (i.e. Null); 1132 Residual
Null Deviance: 1516
Residual Deviance: 1489 AIC: 1495
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.6477 -1.2918 0.8750 0.9619 1.3214
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.145527 0.475505 -0.306 0.7596
currentDF[, PROTEIN] 0.153359 0.062198 2.466 0.0137 *
Age 0.002740 0.006747 0.406 0.6847
Gendermale 0.605104 0.132160 4.579 4.68e-06 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1515.7 on 1134 degrees of freedom
Residual deviance: 1489.0 on 1131 degrees of freedom
AIC: 1497
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_rank ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6R_pg_ug_2015_rank
Trait/outcome.............: IPH
Effect size...............: 0.153359
Standard error............: 0.062198
Odds ratio (effect size)..: 1.166
Lower 95% CI..............: 1.032
Upper 95% CI..............: 1.317
Z-value...................: 2.46566
P-value...................: 0.0136761
Hosmer and Lemeshow r^2...: 0.017572
Cox and Snell r^2.........: 0.023192
Nagelkerke's pseudo r^2...: 0.031471
Sample size of AE DB......: 2388
Sample size of model......: 1135
Missing data %............: 52.47069
Analysis of MCP1_pg_ug_2015_rank.
- processing CalcificationPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender, family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Age Gendermale
-0.87403 -0.45247 0.01396 -0.19322
Degrees of Freedom: 1178 Total (i.e. Null); 1175 Residual
Null Deviance: 1634
Residual Deviance: 1571 AIC: 1579
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.7850 -1.1164 -0.7578 1.1280 1.9083
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.874028 0.460395 -1.898 0.0576 .
currentDF[, PROTEIN] -0.452467 0.062870 -7.197 6.16e-13 ***
Age 0.013956 0.006536 2.135 0.0327 *
Gendermale -0.193224 0.129705 -1.490 0.1363
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1633.9 on 1178 degrees of freedom
Residual deviance: 1571.0 on 1175 degrees of freedom
AIC: 1579
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: CalcificationPlaque
Effect size...............: -0.452467
Standard error............: 0.06287
Odds ratio (effect size)..: 0.636
Lower 95% CI..............: 0.562
Upper 95% CI..............: 0.719
Z-value...................: -7.196888
P-value...................: 6.160221e-13
Hosmer and Lemeshow r^2...: 0.03853
Cox and Snell r^2.........: 0.051997
Nagelkerke's pseudo r^2...: 0.069339
Sample size of AE DB......: 2388
Sample size of model......: 1179
Missing data %............: 50.62814
- processing CollagenPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN],
family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN]
1.3322 -0.2276
Degrees of Freedom: 1180 Total (i.e. Null); 1179 Residual
Null Deviance: 1217
Residual Deviance: 1207 AIC: 1211
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.0999 0.5754 0.6578 0.7138 0.9467
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.210693 0.546856 2.214 0.02683 *
currentDF[, PROTEIN] -0.227373 0.072305 -3.145 0.00166 **
Age 0.001751 0.007781 0.225 0.82191
Gendermale 0.001944 0.155632 0.012 0.99004
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1216.6 on 1180 degrees of freedom
Residual deviance: 1206.5 on 1177 degrees of freedom
AIC: 1214.5
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: CollagenPlaque
Effect size...............: -0.227373
Standard error............: 0.072305
Odds ratio (effect size)..: 0.797
Lower 95% CI..............: 0.691
Upper 95% CI..............: 0.918
Z-value...................: -3.144645
P-value...................: 0.001662884
Hosmer and Lemeshow r^2...: 0.008293
Cox and Snell r^2.........: 0.008507
Nagelkerke's pseudo r^2...: 0.013229
Sample size of AE DB......: 2388
Sample size of model......: 1181
Missing data %............: 50.54439
- processing Fat10Perc
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender, family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Age Gendermale
-0.33492 0.16339 0.01125 0.84917
Degrees of Freedom: 1180 Total (i.e. Null); 1177 Residual
Null Deviance: 1384
Residual Deviance: 1336 AIC: 1344
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.9529 -1.2790 0.6795 0.7714 1.2276
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.334921 0.503185 -0.666 0.5057
currentDF[, PROTEIN] 0.163390 0.066951 2.440 0.0147 *
Age 0.011250 0.007195 1.564 0.1179
Gendermale 0.849174 0.137286 6.185 6.19e-10 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1383.8 on 1180 degrees of freedom
Residual deviance: 1336.3 on 1177 degrees of freedom
AIC: 1344.3
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: Fat10Perc
Effect size...............: 0.16339
Standard error............: 0.066951
Odds ratio (effect size)..: 1.177
Lower 95% CI..............: 1.033
Upper 95% CI..............: 1.343
Z-value...................: 2.440433
P-value...................: 0.01466968
Hosmer and Lemeshow r^2...: 0.034383
Cox and Snell r^2.........: 0.039487
Nagelkerke's pseudo r^2...: 0.057213
Sample size of AE DB......: 2388
Sample size of model......: 1181
Missing data %............: 50.54439
- processing IPH
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Gender, family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Gendermale
0.02354 -0.12418 0.62708
Degrees of Freedom: 1178 Total (i.e. Null); 1176 Residual
Null Deviance: 1576
Residual Deviance: 1549 AIC: 1555
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.6449 -1.2648 0.8841 0.9534 1.3387
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.166656 0.462054 -0.361 0.7183
currentDF[, PROTEIN] -0.123887 0.060696 -2.041 0.0412 *
Age 0.002781 0.006577 0.423 0.6725
Gendermale 0.626312 0.128886 4.859 1.18e-06 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1576.2 on 1178 degrees of freedom
Residual deviance: 1549.0 on 1175 degrees of freedom
AIC: 1557
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: IPH
Effect size...............: -0.123887
Standard error............: 0.060696
Odds ratio (effect size)..: 0.883
Lower 95% CI..............: 0.784
Upper 95% CI..............: 0.995
Z-value...................: -2.041093
P-value...................: 0.04124156
Hosmer and Lemeshow r^2...: 0.017232
Cox and Snell r^2.........: 0.022773
Nagelkerke's pseudo r^2...: 0.030886
Sample size of AE DB......: 2388
Sample size of model......: 1179
Missing data %............: 50.62814 cat("Edit the column names...\n")Edit the column names...colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
"Beta", "s.e.m.",
"OR", "low95CI", "up95CI",
"Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")
cat("Correct the variable types...\n")Correct the variable types...GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)
# Save the data
cat("Writing results to Excel-file...\n")Writing results to Excel-file...### Univariate
write.xlsx(GLM.results,
file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Uni.Protein.PlaquePhenotypes.RANK.MODEL1.xlsx"),
row.names = FALSE, col.names = TRUE, sheetName = "Bin.Uni.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")Removing intermediate files...rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)
Model 2
In this model we correct for Age, Gender, Hypertension status, Diabetes status, current smoker status, lipid-lowering drugs (LLDs), antiplatelet medication, eGFR (MDRD), BMI, CAD history, stroke history, peripheral interventions, and stenosis.
Natural log-transformed data
First we use the natural-log transformed data.
Analysis of continuous/quantitative plaque traits as a function of serum/plaque IL6(R)/MCP1 levels.
GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")Running linear regression...for (protein in 1:length(TRAITS.PROTEIN)) {
PROTEIN = TRAITS.PROTEIN[protein]
cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
for (trait in 1:length(TRAITS.CON)) {
TRAIT = TRAITS.CON[trait]
cat(paste0("\n- processing ",TRAIT,"\n\n"))
currentDF <- as.data.frame(AEDB.CEA %>%
dplyr::select(., PROTEIN, TRAIT, COVARIATES_M2) %>%
filter(complete.cases(.))) %>%
filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
# for debug
# print(DT::datatable(currentDF))
# print(nrow(currentDF))
# print(str(currentDF))
### univariate
fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender +
Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose,
data = currentDF)
model_step <- stepAIC(fit, direction = "both", trace = FALSE)
print(model_step)
print(summary(fit))
GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
GLM.results.TEMP[1,] = GLM.CON(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
GLM.results = rbind(GLM.results, GLM.results.TEMP)
}
}
Analysis of IL6_LN.
- processing Macrophages_LNNote: Using an external vector in selections is ambiguous.
[34mℹ[39m Use `all_of(COVARIATES_M2)` instead of `COVARIATES_M2` to silence this message.
[34mℹ[39m See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
[90mThis message is displayed once per session.[39m
Call:
lm(formula = currentDF[, PROTEIN] ~ Gender + Med.all.antiplatelet +
GFR_MDRD + CAD_history, data = currentDF)
Coefficients:
(Intercept) Gendermale Med.all.antiplateletyes GFR_MDRD CAD_history
4.103909 -0.284834 -0.271084 0.004152 0.163807
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose,
data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.9032 -0.7308 -0.0446 0.6661 4.5590
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.328129 1.065723 5.000 8.77e-07 ***
currentDF[, TRAIT] -0.033490 0.028026 -1.195 0.2328
Age -0.001666 0.007477 -0.223 0.8238
Gendermale -0.298080 0.127682 -2.335 0.0201 *
Hypertension.compositeyes 0.020986 0.169028 0.124 0.9013
DiabetesStatusDiabetes 0.067662 0.142911 0.473 0.6362
SmokerCurrentyes 0.023604 0.122913 0.192 0.8478
Med.Statin.LLDyes -0.136625 0.127000 -1.076 0.2827
Med.all.antiplateletyes -0.319954 0.197708 -1.618 0.1064
GFR_MDRD 0.004767 0.003269 1.458 0.1456
BMI -0.008503 0.015540 -0.547 0.5846
CAD_history 0.210670 0.122440 1.721 0.0861 .
Stroke_history 0.046045 0.116922 0.394 0.6939
Peripheral.interv -0.044833 0.137875 -0.325 0.7452
stenose50-70% -0.896091 0.705483 -1.270 0.2048
stenose70-90% -0.913710 0.641683 -1.424 0.1553
stenose90-99% -0.866203 0.639556 -1.354 0.1764
stenose100% (Occlusion) -1.283157 0.814781 -1.575 0.1161
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.079 on 382 degrees of freedom
Multiple R-squared: 0.03732, Adjusted R-squared: -0.005525
F-statistic: 0.871 on 17 and 382 DF, p-value: 0.6091
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_LN ' with ' Macrophages_LN ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_LN
Trait/outcome.............: Macrophages_LN
Effect size...............: -0.03349
Standard error............: 0.028026
Odds ratio (effect size)..: 0.967
Lower 95% CI..............: 0.915
Upper 95% CI..............: 1.022
T-value...................: -1.194968
P-value...................: 0.2328408
R^2.......................: 0.037317
Adjusted r^2..............: -0.005525
Sample size of AE DB......: 2388
Sample size of model......: 400
Missing data %............: 83.24958
- processing SMC_LN
Call:
lm(formula = currentDF[, PROTEIN] ~ Gender + GFR_MDRD + CAD_history,
data = currentDF)
Coefficients:
(Intercept) Gendermale GFR_MDRD CAD_history
3.833812 -0.284067 0.004641 0.185825
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose,
data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.9791 -0.7354 -0.0493 0.6983 4.5102
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.631403 1.039466 5.418 1.06e-07 ***
currentDF[, TRAIT] 0.008979 0.034238 0.262 0.7933
Age -0.003700 0.007322 -0.505 0.6136
Gendermale -0.307441 0.124996 -2.460 0.0143 *
Hypertension.compositeyes -0.017251 0.161017 -0.107 0.9147
DiabetesStatusDiabetes 0.041644 0.139749 0.298 0.7659
SmokerCurrentyes -0.022435 0.120142 -0.187 0.8520
Med.Statin.LLDyes -0.133992 0.123235 -1.087 0.2776
Med.all.antiplateletyes -0.289321 0.190195 -1.521 0.1290
GFR_MDRD 0.005066 0.003233 1.567 0.1179
BMI -0.012998 0.014999 -0.867 0.3867
CAD_history 0.210577 0.121129 1.738 0.0829 .
Stroke_history 0.029454 0.114635 0.257 0.7974
Peripheral.interv 0.005405 0.137903 0.039 0.9688
stenose50-70% -0.969032 0.692204 -1.400 0.1623
stenose70-90% -0.907907 0.634095 -1.432 0.1530
stenose90-99% -0.829748 0.631393 -1.314 0.1896
stenose100% (Occlusion) -1.259466 0.804880 -1.565 0.1184
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.066 on 388 degrees of freedom
Multiple R-squared: 0.03691, Adjusted R-squared: -0.005284
F-statistic: 0.8748 on 17 and 388 DF, p-value: 0.6046
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_LN ' with ' SMC_LN ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_LN
Trait/outcome.............: SMC_LN
Effect size...............: 0.008979
Standard error............: 0.034238
Odds ratio (effect size)..: 1.009
Lower 95% CI..............: 0.944
Upper 95% CI..............: 1.079
T-value...................: 0.262263
P-value...................: 0.7932581
R^2.......................: 0.036913
Adjusted r^2..............: -0.005284
Sample size of AE DB......: 2388
Sample size of model......: 406
Missing data %............: 82.99832
- processing VesselDensity_LN
Call:
lm(formula = currentDF[, PROTEIN] ~ Gender + GFR_MDRD + CAD_history,
data = currentDF)
Coefficients:
(Intercept) Gendermale GFR_MDRD CAD_history
3.756093 -0.247568 0.005238 0.172172
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose,
data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.9331 -0.7357 -0.0333 0.7108 4.5826
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.629836 1.065900 5.282 2.15e-07 ***
currentDF[, TRAIT] -0.024028 0.084334 -0.285 0.7759
Age -0.004541 0.007427 -0.611 0.5413
Gendermale -0.269618 0.126774 -2.127 0.0341 *
Hypertension.compositeyes 0.001877 0.167583 0.011 0.9911
DiabetesStatusDiabetes 0.067339 0.144271 0.467 0.6409
SmokerCurrentyes -0.007169 0.122842 -0.058 0.9535
Med.Statin.LLDyes -0.127639 0.125637 -1.016 0.3103
Med.all.antiplateletyes -0.254284 0.196881 -1.292 0.1973
GFR_MDRD 0.005475 0.003304 1.657 0.0984 .
BMI -0.013560 0.015302 -0.886 0.3761
CAD_history 0.203887 0.123226 1.655 0.0988 .
Stroke_history 0.029287 0.116544 0.251 0.8017
Peripheral.interv 0.001324 0.139245 0.010 0.9924
stenose50-70% -0.966805 0.702240 -1.377 0.1694
stenose70-90% -0.915169 0.643578 -1.422 0.1558
stenose90-99% -0.847577 0.641374 -1.322 0.1871
stenose100% (Occlusion) -0.915754 0.854866 -1.071 0.2847
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.082 on 382 degrees of freedom
Multiple R-squared: 0.03248, Adjusted R-squared: -0.01058
F-statistic: 0.7542 on 17 and 382 DF, p-value: 0.7457
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_LN ' with ' VesselDensity_LN ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_LN
Trait/outcome.............: VesselDensity_LN
Effect size...............: -0.024028
Standard error............: 0.084334
Odds ratio (effect size)..: 0.976
Lower 95% CI..............: 0.828
Upper 95% CI..............: 1.152
T-value...................: -0.284921
P-value...................: 0.7758592
R^2.......................: 0.032475
Adjusted r^2..............: -0.010582
Sample size of AE DB......: 2388
Sample size of model......: 400
Missing data %............: 83.24958
Analysis of MCP1_LN.
- processing Macrophages_LN
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + Med.Statin.LLD + Med.all.antiplatelet,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age Gendermale Hypertension.compositeyes
5.103134 0.043436 -0.006408 0.203966 -0.195643
Med.Statin.LLDyes Med.all.antiplateletyes
-0.223055 0.201318
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose,
data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.2496 -0.5165 0.0401 0.5857 1.9749
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.910e+00 7.475e-01 7.906 1.86e-14 ***
currentDF[, TRAIT] 4.105e-02 2.006e-02 2.046 0.04131 *
Age -9.148e-03 5.079e-03 -1.801 0.07234 .
Gendermale 1.826e-01 8.723e-02 2.094 0.03681 *
Hypertension.compositeyes -2.086e-01 1.170e-01 -1.783 0.07527 .
DiabetesStatusDiabetes -3.236e-02 9.732e-02 -0.333 0.73965
SmokerCurrentyes -7.826e-02 8.394e-02 -0.932 0.35162
Med.Statin.LLDyes -2.377e-01 8.938e-02 -2.660 0.00808 **
Med.all.antiplateletyes 1.718e-01 1.384e-01 1.241 0.21507
GFR_MDRD -1.361e-05 2.153e-03 -0.006 0.99496
BMI -1.398e-02 1.043e-02 -1.340 0.18093
CAD_history 1.112e-01 8.576e-02 1.297 0.19533
Stroke_history 5.039e-02 8.112e-02 0.621 0.53476
Peripheral.interv -1.334e-01 1.007e-01 -1.325 0.18578
stenose50-70% -4.240e-01 5.363e-01 -0.791 0.42956
stenose70-90% -1.815e-01 4.951e-01 -0.367 0.71402
stenose90-99% -1.605e-01 4.939e-01 -0.325 0.74531
stenose100% (Occlusion) -7.515e-01 6.258e-01 -1.201 0.23035
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8387 on 476 degrees of freedom
Multiple R-squared: 0.06479, Adjusted R-squared: 0.03139
F-statistic: 1.94 on 17 and 476 DF, p-value: 0.01348
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_LN ' with ' Macrophages_LN ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_LN
Trait/outcome.............: Macrophages_LN
Effect size...............: 0.041046
Standard error............: 0.020062
Odds ratio (effect size)..: 1.042
Lower 95% CI..............: 1.002
Upper 95% CI..............: 1.084
T-value...................: 2.04592
P-value...................: 0.04131326
R^2.......................: 0.064791
Adjusted r^2..............: 0.03139
Sample size of AE DB......: 2388
Sample size of model......: 494
Missing data %............: 79.31323
- processing SMC_LN
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + Med.Statin.LLD + Med.all.antiplatelet +
CAD_history, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age Gendermale Hypertension.compositeyes
5.38138 -0.11600 -0.01216 0.18033 -0.20127
Med.Statin.LLDyes Med.all.antiplateletyes CAD_history
-0.21292 0.23166 0.12469
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose,
data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.07018 -0.53655 0.03467 0.56632 2.02007
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 6.127787 0.724585 8.457 3.28e-16 ***
currentDF[, TRAIT] -0.114944 0.023405 -4.911 1.24e-06 ***
Age -0.014207 0.004927 -2.884 0.00411 **
Gendermale 0.167671 0.084432 1.986 0.04761 *
Hypertension.compositeyes -0.200013 0.111043 -1.801 0.07229 .
DiabetesStatusDiabetes -0.058669 0.094300 -0.622 0.53413
SmokerCurrentyes -0.090731 0.080994 -1.120 0.26318
Med.Statin.LLDyes -0.212011 0.085906 -2.468 0.01393 *
Med.all.antiplateletyes 0.191079 0.131899 1.449 0.14807
GFR_MDRD 0.000200 0.002098 0.095 0.92410
BMI -0.012451 0.009998 -1.245 0.21363
CAD_history 0.147609 0.083959 1.758 0.07936 .
Stroke_history 0.059535 0.078689 0.757 0.44967
Peripheral.interv -0.131218 0.098917 -1.327 0.18528
stenose50-70% -0.391836 0.520961 -0.752 0.45233
stenose70-90% -0.194782 0.483665 -0.403 0.68733
stenose90-99% -0.192270 0.482198 -0.399 0.69026
stenose100% (Occlusion) -0.888011 0.610951 -1.453 0.14674
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8194 on 483 degrees of freedom
Multiple R-squared: 0.1051, Adjusted R-squared: 0.07359
F-statistic: 3.336 on 17 and 483 DF, p-value: 8.398e-06
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_LN ' with ' SMC_LN ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_LN
Trait/outcome.............: SMC_LN
Effect size...............: -0.114944
Standard error............: 0.023405
Odds ratio (effect size)..: 0.891
Lower 95% CI..............: 0.851
Upper 95% CI..............: 0.933
T-value...................: -4.911116
P-value...................: 1.241414e-06
R^2.......................: 0.105086
Adjusted r^2..............: 0.073588
Sample size of AE DB......: 2388
Sample size of model......: 501
Missing data %............: 79.0201
- processing VesselDensity_LN
Call:
lm(formula = currentDF[, PROTEIN] ~ Age + Gender + Med.Statin.LLD +
Med.all.antiplatelet, data = currentDF)
Coefficients:
(Intercept) Age Gendermale Med.Statin.LLDyes Med.all.antiplateletyes
4.888582 -0.007793 0.255881 -0.203922 0.244368
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose,
data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.2320 -0.5447 0.0209 0.5708 2.0150
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.8306386 0.7488051 7.787 4.35e-14 ***
currentDF[, TRAIT] -0.0736783 0.0560723 -1.314 0.18948
Age -0.0098492 0.0050290 -1.958 0.05076 .
Gendermale 0.2312103 0.0868748 2.661 0.00804 **
Hypertension.compositeyes -0.1699790 0.1163269 -1.461 0.14462
DiabetesStatusDiabetes -0.0201171 0.0977882 -0.206 0.83710
SmokerCurrentyes -0.0917532 0.0839957 -1.092 0.27523
Med.Statin.LLDyes -0.2113847 0.0886662 -2.384 0.01752 *
Med.all.antiplateletyes 0.2070417 0.1380296 1.500 0.13428
GFR_MDRD 0.0003182 0.0021989 0.145 0.88501
BMI -0.0119131 0.0103569 -1.150 0.25061
CAD_history 0.1289650 0.0869698 1.483 0.13877
Stroke_history 0.0606234 0.0813170 0.746 0.45633
Peripheral.interv -0.1181870 0.1019860 -1.159 0.24710
stenose50-70% -0.4420833 0.5339946 -0.828 0.40815
stenose70-90% -0.1544408 0.4958562 -0.311 0.75559
stenose90-99% -0.1365629 0.4945868 -0.276 0.78258
stenose100% (Occlusion) -0.7433279 0.6260776 -1.187 0.23571
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8397 on 475 degrees of freedom
Multiple R-squared: 0.06341, Adjusted R-squared: 0.02989
F-statistic: 1.892 on 17 and 475 DF, p-value: 0.01684
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_LN ' with ' VesselDensity_LN ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_LN
Trait/outcome.............: VesselDensity_LN
Effect size...............: -0.073678
Standard error............: 0.056072
Odds ratio (effect size)..: 0.929
Lower 95% CI..............: 0.832
Upper 95% CI..............: 1.037
T-value...................: -1.313989
P-value...................: 0.1894841
R^2.......................: 0.06341
Adjusted r^2..............: 0.02989
Sample size of AE DB......: 2388
Sample size of model......: 493
Missing data %............: 79.35511
Analysis of IL6_pg_ug_2015_LN.
- processing Macrophages_LN
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Hypertension.composite +
SmokerCurrent + GFR_MDRD + CAD_history + Stroke_history +
Peripheral.interv, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Hypertension.compositeyes SmokerCurrentyes GFR_MDRD
-2.690821 0.102632 -0.223751 0.160849 -0.003282
CAD_history Stroke_history Peripheral.interv
-0.191410 0.240282 -0.212187
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose,
data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-6.7026 -0.9256 0.0110 0.8741 4.8263
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2.0771142 0.9346824 -2.222 0.026499 *
currentDF[, TRAIT] 0.0985178 0.0257145 3.831 0.000136 ***
Age -0.0060114 0.0057952 -1.037 0.299858
Gendermale 0.0071293 0.1033093 0.069 0.944997
Hypertension.compositeyes -0.1970016 0.1441350 -1.367 0.172014
DiabetesStatusDiabetes 0.0009241 0.1129358 0.008 0.993473
SmokerCurrentyes 0.1158940 0.1040658 1.114 0.265705
Med.Statin.LLDyes -0.0907882 0.1142685 -0.795 0.427092
Med.all.antiplateletyes -0.0191074 0.1538261 -0.124 0.901172
GFR_MDRD -0.0040316 0.0024571 -1.641 0.101172
BMI -0.0135699 0.0129652 -1.047 0.295528
CAD_history -0.1594178 0.1058927 -1.505 0.132535
Stroke_history 0.2574278 0.0996182 2.584 0.009909 **
Peripheral.interv -0.2109443 0.1231333 -1.713 0.087011 .
stenose50-70% -0.0560786 0.6716629 -0.083 0.933478
stenose70-90% 0.3243743 0.6488074 0.500 0.617222
stenose90-99% 0.2435948 0.6485229 0.376 0.707287
stenose100% (Occlusion) 0.9026959 0.8476817 1.065 0.287190
stenose50-99% 0.0402124 1.2045494 0.033 0.973376
stenose70-99% -0.2334215 0.8737346 -0.267 0.789408
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.437 on 956 degrees of freedom
Multiple R-squared: 0.04869, Adjusted R-squared: 0.02979
F-statistic: 2.575 on 19 and 956 DF, p-value: 0.0002445
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_LN ' with ' Macrophages_LN ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_pg_ug_2015_LN
Trait/outcome.............: Macrophages_LN
Effect size...............: 0.098518
Standard error............: 0.025715
Odds ratio (effect size)..: 1.104
Lower 95% CI..............: 1.049
Upper 95% CI..............: 1.161
T-value...................: 3.831213
P-value...................: 0.0001358352
R^2.......................: 0.048694
Adjusted r^2..............: 0.029787
Sample size of AE DB......: 2388
Sample size of model......: 976
Missing data %............: 59.12898
- processing SMC_LN
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
CAD_history + Stroke_history + Peripheral.interv, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age CAD_history Stroke_history Peripheral.interv
-2.49336 -0.14407 -0.01057 -0.20573 0.27847 -0.21314
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose,
data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-6.4261 -0.9451 0.0344 0.9068 5.2333
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2.078646 0.931604 -2.231 0.02589 *
currentDF[, TRAIT] -0.147154 0.031400 -4.686 3.18e-06 ***
Age -0.011799 0.005781 -2.041 0.04154 *
Gendermale -0.041451 0.103403 -0.401 0.68860
Hypertension.compositeyes -0.120224 0.142206 -0.845 0.39808
DiabetesStatusDiabetes -0.006793 0.112792 -0.060 0.95199
SmokerCurrentyes 0.098763 0.103683 0.953 0.34105
Med.Statin.LLDyes -0.085970 0.112710 -0.763 0.44580
Med.all.antiplateletyes -0.028053 0.153119 -0.183 0.85467
GFR_MDRD -0.003391 0.002467 -1.375 0.16953
BMI -0.011670 0.012829 -0.910 0.36324
CAD_history -0.158405 0.106023 -1.494 0.13549
Stroke_history 0.278403 0.099099 2.809 0.00506 **
Peripheral.interv -0.217661 0.123065 -1.769 0.07727 .
stenose50-70% 0.015236 0.672137 0.023 0.98192
stenose70-90% 0.450594 0.649635 0.694 0.48809
stenose90-99% 0.396568 0.649397 0.611 0.54156
stenose100% (Occlusion) 1.062603 0.825135 1.288 0.19813
stenose50-99% 0.298635 1.206918 0.247 0.80462
stenose70-99% -0.047446 0.875053 -0.054 0.95677
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.438 on 966 degrees of freedom
Multiple R-squared: 0.0553, Adjusted R-squared: 0.03672
F-statistic: 2.976 on 19 and 966 DF, p-value: 1.994e-05
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_LN ' with ' SMC_LN ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_pg_ug_2015_LN
Trait/outcome.............: SMC_LN
Effect size...............: -0.147154
Standard error............: 0.0314
Odds ratio (effect size)..: 0.863
Lower 95% CI..............: 0.812
Upper 95% CI..............: 0.918
T-value...................: -4.686438
P-value...................: 3.178718e-06
R^2.......................: 0.055301
Adjusted r^2..............: 0.03672
Sample size of AE DB......: 2388
Sample size of model......: 986
Missing data %............: 58.71022
- processing VesselDensity_LN
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Hypertension.composite +
SmokerCurrent + GFR_MDRD + CAD_history + Stroke_history +
Peripheral.interv, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Hypertension.compositeyes SmokerCurrentyes GFR_MDRD
-2.624708 -0.097253 -0.220997 0.182052 -0.004191
CAD_history Stroke_history Peripheral.interv
-0.190862 0.263254 -0.199689
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose,
data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-6.6531 -0.8703 -0.0006 0.8905 5.6434
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2.038883 0.974633 -2.092 0.03673 *
currentDF[, TRAIT] -0.104738 0.057219 -1.830 0.06752 .
Age -0.005291 0.006093 -0.868 0.38542
Gendermale -0.021403 0.109072 -0.196 0.84448
Hypertension.compositeyes -0.192809 0.151642 -1.271 0.20390
DiabetesStatusDiabetes -0.042885 0.122174 -0.351 0.72566
SmokerCurrentyes 0.131445 0.110742 1.187 0.23557
Med.Statin.LLDyes -0.052907 0.119575 -0.442 0.65827
Med.all.antiplateletyes -0.042817 0.168204 -0.255 0.79913
GFR_MDRD -0.004739 0.002643 -1.793 0.07335 .
BMI -0.011840 0.013579 -0.872 0.38349
CAD_history -0.158915 0.114224 -1.391 0.16450
Stroke_history 0.283760 0.105814 2.682 0.00746 **
Peripheral.interv -0.201818 0.133898 -1.507 0.13211
stenose50-70% -0.167862 0.687564 -0.244 0.80718
stenose70-90% 0.277449 0.659841 0.420 0.67424
stenose90-99% 0.218711 0.658975 0.332 0.74005
stenose100% (Occlusion) 0.890786 0.861237 1.034 0.30128
stenose50-99% 0.096182 1.222682 0.079 0.93732
stenose70-99% -0.592495 0.982979 -0.603 0.54683
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.458 on 870 degrees of freedom
Multiple R-squared: 0.03891, Adjusted R-squared: 0.01792
F-statistic: 1.854 on 19 and 870 DF, p-value: 0.0145
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_LN ' with ' VesselDensity_LN ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_pg_ug_2015_LN
Trait/outcome.............: VesselDensity_LN
Effect size...............: -0.104738
Standard error............: 0.057219
Odds ratio (effect size)..: 0.901
Lower 95% CI..............: 0.805
Upper 95% CI..............: 1.007
T-value...................: -1.830485
P-value...................: 0.06751939
R^2.......................: 0.038911
Adjusted r^2..............: 0.017922
Sample size of AE DB......: 2388
Sample size of model......: 890
Missing data %............: 62.73032
Analysis of IL6R_pg_ug_2015_LN.
- processing Macrophages_LN
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
DiabetesStatus + Med.Statin.LLD + GFR_MDRD + CAD_history +
Stroke_history + Peripheral.interv + stenose, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age DiabetesStatusDiabetes Med.Statin.LLDyes
-1.868663 0.087162 -0.007644 -0.136979 -0.361825
GFR_MDRD CAD_history Stroke_history Peripheral.interv stenose50-70%
-0.002931 -0.119930 0.134121 0.264316 0.578941
stenose70-90% stenose90-99% stenose100% (Occlusion) stenose50-99% stenose70-99%
0.889927 1.048488 0.798484 0.804088 -0.153072
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose,
data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-7.3596 -0.5317 0.1550 0.6632 2.8575
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.614178 0.774058 -2.085 0.0373 *
currentDF[, TRAIT] 0.089191 0.019929 4.475 8.54e-06 ***
Age -0.007346 0.004565 -1.609 0.1079
Gendermale -0.078883 0.080897 -0.975 0.3298
Hypertension.compositeyes 0.016735 0.111904 0.150 0.8812
DiabetesStatusDiabetes -0.116262 0.088332 -1.316 0.1884
SmokerCurrentyes 0.040877 0.081212 0.503 0.6148
Med.Statin.LLDyes -0.366933 0.089831 -4.085 4.78e-05 ***
Med.all.antiplateletyes 0.026960 0.121528 0.222 0.8245
GFR_MDRD -0.002836 0.001955 -1.451 0.1471
BMI -0.010516 0.010370 -1.014 0.3108
CAD_history -0.099551 0.082611 -1.205 0.2285
Stroke_history 0.124764 0.077736 1.605 0.1088
Peripheral.interv 0.252383 0.096699 2.610 0.0092 **
stenose50-70% 0.583432 0.583652 1.000 0.3177
stenose70-90% 0.889630 0.566776 1.570 0.1168
stenose90-99% 1.045135 0.566634 1.844 0.0654 .
stenose100% (Occlusion) 0.794176 0.708635 1.121 0.2627
stenose50-99% 0.788728 0.795545 0.991 0.3217
stenose70-99% -0.171338 0.728191 -0.235 0.8140
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.122 on 957 degrees of freedom
Multiple R-squared: 0.07669, Adjusted R-squared: 0.05836
F-statistic: 4.184 on 19 and 957 DF, p-value: 5.997e-09
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_LN ' with ' Macrophages_LN ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6R_pg_ug_2015_LN
Trait/outcome.............: Macrophages_LN
Effect size...............: 0.089191
Standard error............: 0.019929
Odds ratio (effect size)..: 1.093
Lower 95% CI..............: 1.051
Upper 95% CI..............: 1.137
T-value...................: 4.475492
P-value...................: 8.54016e-06
R^2.......................: 0.076694
Adjusted r^2..............: 0.058363
Sample size of AE DB......: 2388
Sample size of model......: 977
Missing data %............: 59.0871
- processing SMC_LN
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
DiabetesStatus + Med.Statin.LLD + GFR_MDRD + CAD_history +
Stroke_history + Peripheral.interv + stenose, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age DiabetesStatusDiabetes Med.Statin.LLDyes
-1.944179 0.043465 -0.008985 -0.147927 -0.358007
GFR_MDRD CAD_history Stroke_history Peripheral.interv stenose50-70%
-0.002963 -0.118982 0.153593 0.256277 0.609580
stenose70-90% stenose90-99% stenose100% (Occlusion) stenose50-99% stenose70-99%
0.940725 1.100813 0.781036 0.786098 -0.113250
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose,
data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-7.4811 -0.5217 0.1442 0.6521 2.9130
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.861309 0.772888 -2.408 0.01622 *
currentDF[, TRAIT] 0.042720 0.024761 1.725 0.08479 .
Age -0.008717 0.004564 -1.910 0.05646 .
Gendermale -0.016853 0.080912 -0.208 0.83505
Hypertension.compositeyes 0.031667 0.110631 0.286 0.77476
DiabetesStatusDiabetes -0.138529 0.088243 -1.570 0.11677
SmokerCurrentyes 0.036806 0.081052 0.454 0.64985
Med.Statin.LLDyes -0.360922 0.088737 -4.067 5.14e-05 ***
Med.all.antiplateletyes 0.025253 0.121171 0.208 0.83496
GFR_MDRD -0.002975 0.001966 -1.513 0.13049
BMI -0.006163 0.010272 -0.600 0.54867
CAD_history -0.112193 0.082810 -1.355 0.17579
Stroke_history 0.148217 0.077459 1.913 0.05598 .
Peripheral.interv 0.250446 0.096792 2.587 0.00981 **
stenose50-70% 0.623885 0.585257 1.066 0.28669
stenose70-90% 0.953389 0.568526 1.677 0.09388 .
stenose90-99% 1.110673 0.568374 1.954 0.05097 .
stenose100% (Occlusion) 0.795287 0.694155 1.146 0.25221
stenose50-99% 0.779812 0.798436 0.977 0.32897
stenose70-99% -0.111279 0.730667 -0.152 0.87898
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.126 on 968 degrees of freedom
Multiple R-squared: 0.06282, Adjusted R-squared: 0.04443
F-statistic: 3.415 on 19 and 968 DF, p-value: 1.128e-06
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_LN ' with ' SMC_LN ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6R_pg_ug_2015_LN
Trait/outcome.............: SMC_LN
Effect size...............: 0.04272
Standard error............: 0.024761
Odds ratio (effect size)..: 1.044
Lower 95% CI..............: 0.994
Upper 95% CI..............: 1.096
T-value...................: 1.725318
P-value...................: 0.08478925
R^2.......................: 0.062822
Adjusted r^2..............: 0.044427
Sample size of AE DB......: 2388
Sample size of model......: 988
Missing data %............: 58.62647
- processing VesselDensity_LN
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
DiabetesStatus + Med.Statin.LLD + GFR_MDRD + CAD_history +
Stroke_history + Peripheral.interv + stenose, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age DiabetesStatusDiabetes Med.Statin.LLDyes
-1.985548 0.085060 -0.011047 -0.191927 -0.382204
GFR_MDRD CAD_history Stroke_history Peripheral.interv stenose50-70%
-0.003247 -0.151232 0.134454 0.264760 0.670039
stenose70-90% stenose90-99% stenose100% (Occlusion) stenose50-99% stenose70-99%
1.051640 1.182766 0.884950 0.827384 -0.050675
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose,
data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-7.4644 -0.5178 0.1193 0.6264 2.8616
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.720513 0.805252 -2.137 0.0329 *
currentDF[, TRAIT] 0.084011 0.045007 1.867 0.0623 .
Age -0.011289 0.004793 -2.355 0.0187 *
Gendermale -0.043677 0.084990 -0.514 0.6074
Hypertension.compositeyes 0.029801 0.117703 0.253 0.8002
DiabetesStatusDiabetes -0.178220 0.095291 -1.870 0.0618 .
SmokerCurrentyes 0.015091 0.086217 0.175 0.8611
Med.Statin.LLDyes -0.386904 0.093850 -4.123 4.11e-05 ***
Med.all.antiplateletyes 0.009582 0.132680 0.072 0.9424
GFR_MDRD -0.003231 0.002111 -1.531 0.1262
BMI -0.009544 0.010931 -0.873 0.3828
CAD_history -0.141443 0.088663 -1.595 0.1110
Stroke_history 0.129125 0.082444 1.566 0.1177
Peripheral.interv 0.256497 0.104752 2.449 0.0145 *
stenose50-70% 0.665189 0.594432 1.119 0.2634
stenose70-90% 1.048265 0.573848 1.827 0.0681 .
stenose90-99% 1.176706 0.573255 2.053 0.0404 *
stenose100% (Occlusion) 0.872861 0.717166 1.217 0.2239
stenose50-99% 0.815870 0.804759 1.014 0.3110
stenose70-99% -0.040092 0.807060 -0.050 0.9604
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.135 on 869 degrees of freedom
Multiple R-squared: 0.06919, Adjusted R-squared: 0.04884
F-statistic: 3.4 on 19 and 869 DF, p-value: 1.328e-06
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_LN ' with ' VesselDensity_LN ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6R_pg_ug_2015_LN
Trait/outcome.............: VesselDensity_LN
Effect size...............: 0.084011
Standard error............: 0.045007
Odds ratio (effect size)..: 1.088
Lower 95% CI..............: 0.996
Upper 95% CI..............: 1.188
T-value...................: 1.8666
P-value...................: 0.0622941
R^2.......................: 0.069191
Adjusted r^2..............: 0.04884
Sample size of AE DB......: 2388
Sample size of model......: 889
Missing data %............: 62.77219
Analysis of MCP1_pg_ug_2015_LN.
- processing Macrophages_LN
Call:
lm(formula = currentDF[, PROTEIN] ~ Hypertension.composite +
Med.Statin.LLD + Stroke_history, data = currentDF)
Coefficients:
(Intercept) Hypertension.compositeyes Med.Statin.LLDyes Stroke_history
-0.8522 -0.2802 -0.2096 0.1486
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose,
data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-5.7571 -0.7488 0.0930 0.8590 3.4920
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.925358 0.842505 -1.098 0.2723
currentDF[, TRAIT] -0.026271 0.022722 -1.156 0.2479
Age -0.001425 0.005170 -0.276 0.7830
Gendermale 0.097049 0.091595 1.060 0.2896
Hypertension.compositeyes -0.269214 0.128299 -2.098 0.0361 *
DiabetesStatusDiabetes -0.033010 0.100639 -0.328 0.7430
SmokerCurrentyes -0.009423 0.092386 -0.102 0.9188
Med.Statin.LLDyes -0.208582 0.102743 -2.030 0.0426 *
Med.all.antiplateletyes -0.027788 0.138284 -0.201 0.8408
GFR_MDRD -0.001712 0.002194 -0.780 0.4354
BMI -0.005436 0.011533 -0.471 0.6375
CAD_history -0.061333 0.093962 -0.653 0.5141
Stroke_history 0.140095 0.088863 1.577 0.1152
Peripheral.interv 0.017046 0.109534 0.156 0.8764
stenose50-70% 0.339845 0.610362 0.557 0.5778
stenose70-90% 0.471059 0.589413 0.799 0.4244
stenose90-99% 0.291657 0.589271 0.495 0.6207
stenose100% (Occlusion) -0.477681 0.770245 -0.620 0.5353
stenose50-99% 0.769852 0.878037 0.877 0.3808
stenose70-99% 0.638329 0.793977 0.804 0.4216
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.306 on 996 degrees of freedom
Multiple R-squared: 0.02457, Adjusted R-squared: 0.005962
F-statistic: 1.32 on 19 and 996 DF, p-value: 0.1609
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_LN ' with ' Macrophages_LN ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_LN
Trait/outcome.............: Macrophages_LN
Effect size...............: -0.026271
Standard error............: 0.022722
Odds ratio (effect size)..: 0.974
Lower 95% CI..............: 0.932
Upper 95% CI..............: 1.018
T-value...................: -1.156187
P-value...................: 0.2478818
R^2.......................: 0.02457
Adjusted r^2..............: 0.005962
Sample size of AE DB......: 2388
Sample size of model......: 1016
Missing data %............: 57.45394
- processing SMC_LN
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Hypertension.composite +
Med.Statin.LLD + Stroke_history, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Hypertension.compositeyes Med.Statin.LLDyes Stroke_history
-0.85457 -0.08447 -0.26329 -0.22108 0.12716
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose,
data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-5.7696 -0.7530 0.0762 0.8597 3.5363
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.8994588 0.8361173 -1.076 0.28229
currentDF[, TRAIT] -0.0835184 0.0278491 -2.999 0.00278 **
Age -0.0026486 0.0051357 -0.516 0.60617
Gendermale 0.0664656 0.0912138 0.729 0.46637
Hypertension.compositeyes -0.2411143 0.1260696 -1.913 0.05609 .
DiabetesStatusDiabetes -0.0680869 0.0999108 -0.681 0.49573
SmokerCurrentyes -0.0088623 0.0916328 -0.097 0.92297
Med.Statin.LLDyes -0.2229455 0.1008775 -2.210 0.02733 *
Med.all.antiplateletyes -0.0224478 0.1370340 -0.164 0.86991
GFR_MDRD -0.0009749 0.0021921 -0.445 0.65659
BMI -0.0052354 0.0113634 -0.461 0.64510
CAD_history -0.0618465 0.0935719 -0.661 0.50879
Stroke_history 0.1220969 0.0880165 1.387 0.16569
Peripheral.interv 0.0617444 0.1089423 0.567 0.57100
stenose50-70% 0.3690008 0.6080728 0.607 0.54410
stenose70-90% 0.5043705 0.5875537 0.858 0.39086
stenose90-99% 0.3427729 0.5874487 0.583 0.55969
stenose100% (Occlusion) -0.2187628 0.7464469 -0.293 0.76953
stenose50-99% 0.8500869 0.8754372 0.971 0.33176
stenose70-99% 0.7158192 0.7916197 0.904 0.36608
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.302 on 1007 degrees of freedom
Multiple R-squared: 0.03067, Adjusted R-squared: 0.01238
F-statistic: 1.677 on 19 and 1007 DF, p-value: 0.03435
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_LN ' with ' SMC_LN ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_LN
Trait/outcome.............: SMC_LN
Effect size...............: -0.083518
Standard error............: 0.027849
Odds ratio (effect size)..: 0.92
Lower 95% CI..............: 0.871
Upper 95% CI..............: 0.971
T-value...................: -2.998965
P-value...................: 0.002775574
R^2.......................: 0.030672
Adjusted r^2..............: 0.012383
Sample size of AE DB......: 2388
Sample size of model......: 1027
Missing data %............: 56.9933
- processing VesselDensity_LN
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Hypertension.composite +
Med.Statin.LLD + Stroke_history, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Hypertension.compositeyes Med.Statin.LLDyes Stroke_history
-0.5912 -0.1803 -0.2543 -0.2075 0.1558
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose,
data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-5.6053 -0.7439 0.0709 0.8139 3.6085
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.463146 0.873520 -0.530 0.596099
currentDF[, TRAIT] -0.176119 0.050659 -3.477 0.000532 ***
Age -0.001246 0.005407 -0.230 0.817776
Gendermale 0.138756 0.096023 1.445 0.148796
Hypertension.compositeyes -0.245848 0.135031 -1.821 0.068985 .
DiabetesStatusDiabetes -0.059439 0.108067 -0.550 0.582439
SmokerCurrentyes -0.005477 0.097786 -0.056 0.955347
Med.Statin.LLDyes -0.205808 0.107190 -1.920 0.055167 .
Med.all.antiplateletyes 0.042184 0.150129 0.281 0.778788
GFR_MDRD -0.002446 0.002350 -1.041 0.298184
BMI -0.008293 0.012091 -0.686 0.492954
CAD_history -0.061503 0.100738 -0.611 0.541669
Stroke_history 0.144423 0.093922 1.538 0.124475
Peripheral.interv 0.016866 0.118344 0.143 0.886704
stenose50-70% 0.147602 0.621101 0.238 0.812209
stenose70-90% 0.318132 0.595921 0.534 0.593576
stenose90-99% 0.169478 0.595273 0.285 0.775934
stenose100% (Occlusion) -0.528418 0.778005 -0.679 0.497187
stenose50-99% 0.810463 0.886127 0.915 0.360638
stenose70-99% 0.234863 0.888016 0.264 0.791470
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.318 on 906 degrees of freedom
Multiple R-squared: 0.03654, Adjusted R-squared: 0.01634
F-statistic: 1.809 on 19 and 906 DF, p-value: 0.01817
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_LN ' with ' VesselDensity_LN ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_LN
Trait/outcome.............: VesselDensity_LN
Effect size...............: -0.176119
Standard error............: 0.050659
Odds ratio (effect size)..: 0.839
Lower 95% CI..............: 0.759
Upper 95% CI..............: 0.926
T-value...................: -3.476584
P-value...................: 0.0005319754
R^2.......................: 0.036541
Adjusted r^2..............: 0.016336
Sample size of AE DB......: 2388
Sample size of model......: 926
Missing data %............: 61.22278 cat("Edit the column names...\n")Edit the column names...colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
"Beta", "s.e.m.",
"OR", "low95CI", "up95CI",
"T-value", "P-value", "r^2", "r^2_adj", "AE_N", "Model_N", "Perc_Miss")
cat("Correct the variable types...\n")Correct the variable types...GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)
# Save the data
cat("Writing results to Excel-file...\n")Writing results to Excel-file...### Univariate
library(openxlsx)
write.xlsx(GLM.results,
file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Con.Multi.Protein.PlaquePhenotypes.MODEL2.xlsx"),
row.names = FALSE, col.names = TRUE, sheetName = "Con.Multi.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")Removing intermediate files...rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)
Analysis of binary plaque traits as a function of serum/plaque IL6(R)/MCP1 levels.
GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN)) {
PROTEIN = TRAITS.PROTEIN[protein]
cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
for (trait in 1:length(TRAITS.BIN)) {
TRAIT = TRAITS.BIN[trait]
cat(paste0("\n- processing ",TRAIT,"\n\n"))
currentDF <- as.data.frame(AEDB.CEA %>%
dplyr::select(., PROTEIN, TRAIT, COVARIATES_M2) %>%
filter(complete.cases(.))) %>%
filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
# for debug
# print(DT::datatable(currentDF))
# print(nrow(currentDF))
# print(str(currentDF))
# print(class(currentDF[,TRAIT]))
### univariate
fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender +
Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose,
data = currentDF, family = binomial(link = "logit"))
model_step <- stepAIC(fit, direction = "both", trace = FALSE)
print(model_step)
print(summary(fit))
GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
GLM.results = rbind(GLM.results, GLM.results.TEMP)
}
}
Analysis of IL6_LN.
- processing CalcificationPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Hypertension.composite +
Stroke_history, family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) Hypertension.compositeyes Stroke_history
0.06077 0.42883 -0.31558
Degrees of Freedom: 412 Total (i.e. Null); 410 Residual
Null Deviance: 561.6
Residual Deviance: 557.3 AIC: 563.3
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.9040 -1.2441 0.8325 1.0410 1.5634
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.211560 2.100615 0.101 0.920
currentDF[, PROTEIN] 0.142733 0.098280 1.452 0.146
Age -0.009077 0.014097 -0.644 0.520
Gendermale -0.228810 0.242795 -0.942 0.346
Hypertension.compositeyes 0.506219 0.309468 1.636 0.102
DiabetesStatusDiabetes -0.355035 0.267598 -1.327 0.185
SmokerCurrentyes -0.078917 0.229881 -0.343 0.731
Med.Statin.LLDyes -0.145398 0.239118 -0.608 0.543
Med.all.antiplateletyes 0.309251 0.366705 0.843 0.399
GFR_MDRD -0.010044 0.006340 -1.584 0.113
BMI -0.020982 0.029020 -0.723 0.470
CAD_history 0.061271 0.233414 0.262 0.793
Stroke_history -0.329851 0.219902 -1.500 0.134
Peripheral.interv -0.250407 0.260258 -0.962 0.336
stenose50-70% 1.444119 1.377917 1.048 0.295
stenose70-90% 1.402594 1.268968 1.105 0.269
stenose90-99% 1.071288 1.262660 0.848 0.396
stenose100% (Occlusion) 1.414209 1.589178 0.890 0.374
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 561.62 on 412 degrees of freedom
Residual deviance: 544.61 on 395 degrees of freedom
AIC: 580.61
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_LN ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_LN
Trait/outcome.............: CalcificationPlaque
Effect size...............: 0.142733
Standard error............: 0.09828
Odds ratio (effect size)..: 1.153
Lower 95% CI..............: 0.951
Upper 95% CI..............: 1.398
Z-value...................: 1.452309
P-value...................: 0.1464158
Hosmer and Lemeshow r^2...: 0.030295
Cox and Snell r^2.........: 0.04036
Nagelkerke's pseudo r^2...: 0.054298
Sample size of AE DB......: 2388
Sample size of model......: 413
Missing data %............: 82.70519
- processing CollagenPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ SmokerCurrent +
stenose, family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) SmokerCurrentyes stenose50-70% stenose70-90% stenose90-99%
1.642e+01 4.689e-01 3.571e-09 -1.515e+01 -1.547e+01
stenose100% (Occlusion)
-1.512e+01
Degrees of Freedom: 411 Total (i.e. Null); 406 Residual
Null Deviance: 427.4
Residual Deviance: 413.6 AIC: 425.6
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.12610 0.00028 0.62576 0.74886 1.10870
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.871e+01 2.248e+03 0.008 0.9934
currentDF[, PROTEIN] 1.060e-01 1.182e-01 0.897 0.3696
Age -1.050e-02 1.724e-02 -0.609 0.5426
Gendermale -4.748e-02 2.947e-01 -0.161 0.8720
Hypertension.compositeyes 4.265e-01 3.623e-01 1.177 0.2391
DiabetesStatusDiabetes 2.622e-01 3.339e-01 0.785 0.4323
SmokerCurrentyes 4.934e-01 2.862e-01 1.724 0.0847 .
Med.Statin.LLDyes -1.066e-01 2.892e-01 -0.368 0.7125
Med.all.antiplateletyes 4.696e-01 4.214e-01 1.115 0.2651
GFR_MDRD -1.124e-02 7.906e-03 -1.421 0.1553
BMI -4.181e-02 3.521e-02 -1.188 0.2350
CAD_history 8.257e-02 2.827e-01 0.292 0.7702
Stroke_history 1.151e-01 2.726e-01 0.422 0.6729
Peripheral.interv -4.408e-01 3.013e-01 -1.463 0.1435
stenose50-70% 2.979e-01 2.462e+03 0.000 0.9999
stenose70-90% -1.590e+01 2.248e+03 -0.007 0.9944
stenose90-99% -1.632e+01 2.248e+03 -0.007 0.9942
stenose100% (Occlusion) -1.561e+01 2.248e+03 -0.007 0.9945
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 427.39 on 411 degrees of freedom
Residual deviance: 404.07 on 394 degrees of freedom
AIC: 440.07
Number of Fisher Scoring iterations: 16
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_LN ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_LN
Trait/outcome.............: CollagenPlaque
Effect size...............: 0.106009
Standard error............: 0.118157
Odds ratio (effect size)..: 1.112
Lower 95% CI..............: 0.882
Upper 95% CI..............: 1.402
Z-value...................: 0.897189
P-value...................: 0.3696183
Hosmer and Lemeshow r^2...: 0.054567
Cox and Snell r^2.........: 0.055034
Nagelkerke's pseudo r^2...: 0.085243
Sample size of AE DB......: 2388
Sample size of model......: 412
Missing data %............: 82.74707
- processing Fat10Perc
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Gender + Hypertension.composite +
Stroke_history, family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) Gendermale Hypertension.compositeyes Stroke_history
0.1829 0.6076 0.7569 0.7206
Degrees of Freedom: 412 Total (i.e. Null); 409 Residual
Null Deviance: 403.2
Residual Deviance: 387.6 AIC: 395.6
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.3279 0.3992 0.5515 0.6787 1.2810
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.522e+01 1.375e+03 0.011 0.9912
currentDF[, PROTEIN] 2.553e-02 1.198e-01 0.213 0.8313
Age -1.085e-02 1.794e-02 -0.605 0.5453
Gendermale 6.661e-01 2.899e-01 2.298 0.0216 *
Hypertension.compositeyes 8.268e-01 3.505e-01 2.359 0.0183 *
DiabetesStatusDiabetes -1.988e-01 3.316e-01 -0.599 0.5489
SmokerCurrentyes 3.269e-01 2.994e-01 1.092 0.2750
Med.Statin.LLDyes -1.057e-01 3.071e-01 -0.344 0.7307
Med.all.antiplateletyes 9.866e-02 4.636e-01 0.213 0.8315
GFR_MDRD -7.000e-03 8.346e-03 -0.839 0.4016
BMI 1.383e-02 3.676e-02 0.376 0.7067
CAD_history 1.454e-02 2.982e-01 0.049 0.9611
Stroke_history 6.934e-01 3.085e-01 2.248 0.0246 *
Peripheral.interv -3.647e-01 3.125e-01 -1.167 0.2432
stenose50-70% -1.505e+01 1.375e+03 -0.011 0.9913
stenose70-90% -1.426e+01 1.375e+03 -0.010 0.9917
stenose90-99% -1.446e+01 1.375e+03 -0.011 0.9916
stenose100% (Occlusion) 9.090e-01 1.736e+03 0.001 0.9996
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 403.15 on 412 degrees of freedom
Residual deviance: 377.76 on 395 degrees of freedom
AIC: 413.76
Number of Fisher Scoring iterations: 15
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_LN ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_LN
Trait/outcome.............: Fat10Perc
Effect size...............: 0.025535
Standard error............: 0.119833
Odds ratio (effect size)..: 1.026
Lower 95% CI..............: 0.811
Upper 95% CI..............: 1.297
Z-value...................: 0.213092
P-value...................: 0.8312552
Hosmer and Lemeshow r^2...: 0.062996
Cox and Snell r^2.........: 0.059642
Nagelkerke's pseudo r^2...: 0.095695
Sample size of AE DB......: 2388
Sample size of model......: 413
Missing data %............: 82.70519
- processing IPH
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Gender + Hypertension.composite +
DiabetesStatus + Med.all.antiplatelet, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) Gendermale Hypertension.compositeyes DiabetesStatusDiabetes Med.all.antiplateletyes
1.0660 0.5303 0.5860 -0.5228 -0.7202
Degrees of Freedom: 412 Total (i.e. Null); 408 Residual
Null Deviance: 454.9
Residual Deviance: 441.3 AIC: 451.3
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.0621 0.3753 0.6288 0.7913 1.2205
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -1.145802 2.323801 -0.493 0.6220
currentDF[, PROTEIN] -0.060240 0.109513 -0.550 0.5823
Age 0.013977 0.015971 0.875 0.3815
Gendermale 0.549378 0.264555 2.077 0.0378 *
Hypertension.compositeyes 0.485556 0.335458 1.447 0.1478
DiabetesStatusDiabetes -0.635087 0.295408 -2.150 0.0316 *
SmokerCurrentyes 0.155838 0.269384 0.578 0.5629
Med.Statin.LLDyes 0.073143 0.275737 0.265 0.7908
Med.all.antiplateletyes -0.754014 0.523796 -1.440 0.1500
GFR_MDRD -0.005491 0.007373 -0.745 0.4565
BMI 0.026724 0.032835 0.814 0.4157
CAD_history 0.215890 0.279275 0.773 0.4395
Stroke_history 0.030560 0.255858 0.119 0.9049
Peripheral.interv 0.254194 0.314189 0.809 0.4185
stenose50-70% 1.153696 1.419822 0.813 0.4165
stenose70-90% 1.043858 1.278701 0.816 0.4143
stenose90-99% 1.146267 1.274104 0.900 0.3683
stenose100% (Occlusion) 1.034588 1.741930 0.594 0.5526
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 454.92 on 412 degrees of freedom
Residual deviance: 435.57 on 395 degrees of freedom
AIC: 471.57
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_LN ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_LN
Trait/outcome.............: IPH
Effect size...............: -0.06024
Standard error............: 0.109513
Odds ratio (effect size)..: 0.942
Lower 95% CI..............: 0.76
Upper 95% CI..............: 1.167
Z-value...................: -0.550072
P-value...................: 0.58227
Hosmer and Lemeshow r^2...: 0.042528
Cox and Snell r^2.........: 0.045764
Nagelkerke's pseudo r^2...: 0.068548
Sample size of AE DB......: 2388
Sample size of model......: 413
Missing data %............: 82.70519
Analysis of MCP1_LN.
- processing CalcificationPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
DiabetesStatus + GFR_MDRD + Stroke_history + Peripheral.interv,
family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] DiabetesStatusDiabetes GFR_MDRD Stroke_history
2.3756 -0.2319 -0.4694 -0.0093 -0.3938
Peripheral.interv
-0.3572
Degrees of Freedom: 506 Total (i.e. Null); 501 Residual
Null Deviance: 687.8
Residual Deviance: 670.7 AIC: 682.7
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.8298 -1.2494 0.8377 1.0142 1.5091
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.071917 1.970396 0.036 0.9709
currentDF[, PROTEIN] -0.216531 0.113735 -1.904 0.0569 .
Age 0.005647 0.012345 0.457 0.6474
Gendermale -0.098242 0.214322 -0.458 0.6467
Hypertension.compositeyes 0.390757 0.278309 1.404 0.1603
DiabetesStatusDiabetes -0.521749 0.236227 -2.209 0.0272 *
SmokerCurrentyes 0.164273 0.204971 0.801 0.4229
Med.Statin.LLDyes -0.113143 0.218309 -0.518 0.6043
Med.all.antiplateletyes 0.165116 0.336537 0.491 0.6237
GFR_MDRD -0.008601 0.005339 -1.611 0.1072
BMI 0.005842 0.025430 0.230 0.8183
CAD_history -0.010525 0.211439 -0.050 0.9603
Stroke_history -0.406136 0.197879 -2.052 0.0401 *
Peripheral.interv -0.334416 0.246904 -1.354 0.1756
stenose50-70% 1.282607 1.371998 0.935 0.3499
stenose70-90% 1.447780 1.284020 1.128 0.2595
stenose90-99% 1.153776 1.279820 0.902 0.3673
stenose100% (Occlusion) 1.265528 1.609157 0.786 0.4316
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 687.85 on 506 degrees of freedom
Residual deviance: 664.87 on 489 degrees of freedom
AIC: 700.87
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_LN ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_LN
Trait/outcome.............: CalcificationPlaque
Effect size...............: -0.216531
Standard error............: 0.113735
Odds ratio (effect size)..: 0.805
Lower 95% CI..............: 0.644
Upper 95% CI..............: 1.006
Z-value...................: -1.90382
P-value...................: 0.05693357
Hosmer and Lemeshow r^2...: 0.033411
Cox and Snell r^2.........: 0.044317
Nagelkerke's pseudo r^2...: 0.059687
Sample size of AE DB......: 2388
Sample size of model......: 507
Missing data %............: 78.76884
- processing CollagenPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
SmokerCurrent + Med.all.antiplatelet + Peripheral.interv +
stenose, family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] SmokerCurrentyes Med.all.antiplateletyes Peripheral.interv
18.6959 -0.7585 0.5419 0.6881 -0.5865
stenose50-70% stenose70-90% stenose90-99% stenose100% (Occlusion)
-13.1027 -14.0946 -14.5925 -14.1993
Degrees of Freedom: 504 Total (i.e. Null); 496 Residual
Null Deviance: 499.8
Residual Deviance: 456.3 AIC: 474.3
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.5468 0.3210 0.5145 0.6873 1.3907
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 19.208916 778.940414 0.025 0.9803
currentDF[, PROTEIN] -0.747348 0.160259 -4.663 3.11e-06 ***
Age -0.002509 0.015925 -0.158 0.8748
Gendermale -0.101596 0.281344 -0.361 0.7180
Hypertension.compositeyes 0.242031 0.345102 0.701 0.4831
DiabetesStatusDiabetes 0.295815 0.320385 0.923 0.3558
SmokerCurrentyes 0.563563 0.272956 2.065 0.0390 *
Med.Statin.LLDyes 0.035280 0.270331 0.131 0.8962
Med.all.antiplateletyes 0.687607 0.396855 1.733 0.0832 .
GFR_MDRD -0.002168 0.006966 -0.311 0.7556
BMI -0.019309 0.034070 -0.567 0.5709
CAD_history 0.162940 0.269011 0.606 0.5447
Stroke_history 0.118618 0.258214 0.459 0.6460
Peripheral.interv -0.645900 0.299607 -2.156 0.0311 *
stenose50-70% -13.110963 778.938449 -0.017 0.9866
stenose70-90% -14.120405 778.937755 -0.018 0.9855
stenose90-99% -14.635370 778.937743 -0.019 0.9850
stenose100% (Occlusion) -14.102429 778.938684 -0.018 0.9856
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 499.81 on 504 degrees of freedom
Residual deviance: 453.59 on 487 degrees of freedom
AIC: 489.59
Number of Fisher Scoring iterations: 14
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_LN ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_LN
Trait/outcome.............: CollagenPlaque
Effect size...............: -0.747348
Standard error............: 0.160259
Odds ratio (effect size)..: 0.474
Lower 95% CI..............: 0.346
Upper 95% CI..............: 0.648
Z-value...................: -4.663368
P-value...................: 3.110754e-06
Hosmer and Lemeshow r^2...: 0.092477
Cox and Snell r^2.........: 0.087463
Nagelkerke's pseudo r^2...: 0.139202
Sample size of AE DB......: 2388
Sample size of model......: 505
Missing data %............: 78.8526
- processing Fat10Perc
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Gender + Hypertension.composite + SmokerCurrent + Stroke_history,
family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Gendermale Hypertension.compositeyes SmokerCurrentyes
-3.3934 0.7741 0.5974 0.7455 0.3775
Stroke_history
0.7308
Degrees of Freedom: 506 Total (i.e. Null); 501 Residual
Null Deviance: 495
Residual Deviance: 444.7 AIC: 456.7
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.5589 0.2924 0.4739 0.6631 1.5573
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 8.460e+00 8.131e+02 0.010 0.9917
currentDF[, PROTEIN] 7.856e-01 1.516e-01 5.183 2.18e-07 ***
Age 3.727e-03 1.615e-02 0.231 0.8175
Gendermale 6.253e-01 2.612e-01 2.394 0.0167 *
Hypertension.compositeyes 7.570e-01 3.445e-01 2.198 0.0280 *
DiabetesStatusDiabetes -2.620e-01 3.015e-01 -0.869 0.3848
SmokerCurrentyes 3.903e-01 2.746e-01 1.422 0.1552
Med.Statin.LLDyes -6.708e-02 2.965e-01 -0.226 0.8210
Med.all.antiplateletyes 1.913e-01 4.135e-01 0.463 0.6436
GFR_MDRD 1.764e-04 7.232e-03 0.024 0.9805
BMI 4.233e-02 3.303e-02 1.282 0.1999
CAD_history -2.006e-01 2.787e-01 -0.720 0.4716
Stroke_history 7.218e-01 2.857e-01 2.526 0.0115 *
Peripheral.interv -1.379e-01 3.055e-01 -0.451 0.6516
stenose50-70% -1.418e+01 8.131e+02 -0.017 0.9861
stenose70-90% -1.306e+01 8.131e+02 -0.016 0.9872
stenose90-99% -1.339e+01 8.131e+02 -0.016 0.9869
stenose100% (Occlusion) -1.285e+01 8.131e+02 -0.016 0.9874
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 494.97 on 506 degrees of freedom
Residual deviance: 435.54 on 489 degrees of freedom
AIC: 471.54
Number of Fisher Scoring iterations: 14
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_LN ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_LN
Trait/outcome.............: Fat10Perc
Effect size...............: 0.785582
Standard error............: 0.151558
Odds ratio (effect size)..: 2.194
Lower 95% CI..............: 1.63
Upper 95% CI..............: 2.952
Z-value...................: 5.183379
P-value...................: 2.179012e-07
Hosmer and Lemeshow r^2...: 0.120063
Cox and Snell r^2.........: 0.110605
Nagelkerke's pseudo r^2...: 0.177454
Sample size of AE DB......: 2388
Sample size of model......: 507
Missing data %............: 78.76884
- processing IPH
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + Gender +
DiabetesStatus + BMI + Peripheral.interv, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) Age Gendermale DiabetesStatusDiabetes BMI
-2.11972 0.02062 0.76552 -0.51884 0.05379
Peripheral.interv
0.43188
Degrees of Freedom: 506 Total (i.e. Null); 501 Residual
Null Deviance: 557.2
Residual Deviance: 535.1 AIC: 547.1
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.1222 0.4422 0.6184 0.7615 1.5806
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -3.392924 2.168263 -1.565 0.1176
currentDF[, PROTEIN] 0.131376 0.130297 1.008 0.3133
Age 0.014973 0.014227 1.052 0.2926
Gendermale 0.749485 0.233010 3.217 0.0013 **
Hypertension.compositeyes 0.270763 0.311744 0.869 0.3851
DiabetesStatusDiabetes -0.530889 0.264110 -2.010 0.0444 *
SmokerCurrentyes 0.041490 0.239050 0.174 0.8622
Med.Statin.LLDyes -0.085328 0.258019 -0.331 0.7409
Med.all.antiplateletyes -0.097537 0.397652 -0.245 0.8062
GFR_MDRD -0.005021 0.006267 -0.801 0.4230
BMI 0.051515 0.029392 1.753 0.0797 .
CAD_history 0.187788 0.256176 0.733 0.4635
Stroke_history 0.118708 0.232940 0.510 0.6103
Peripheral.interv 0.418163 0.306767 1.363 0.1728
stenose50-70% 1.262123 1.380939 0.914 0.3607
stenose70-90% 1.184072 1.266946 0.935 0.3500
stenose90-99% 1.383535 1.264436 1.094 0.2739
stenose100% (Occlusion) 1.541946 1.722692 0.895 0.3707
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 557.22 on 506 degrees of freedom
Residual deviance: 529.66 on 489 degrees of freedom
AIC: 565.66
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_LN ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_LN
Trait/outcome.............: IPH
Effect size...............: 0.131376
Standard error............: 0.130297
Odds ratio (effect size)..: 1.14
Lower 95% CI..............: 0.883
Upper 95% CI..............: 1.472
Z-value...................: 1.008281
P-value...................: 0.3133197
Hosmer and Lemeshow r^2...: 0.049464
Cox and Snell r^2.........: 0.052912
Nagelkerke's pseudo r^2...: 0.079351
Sample size of AE DB......: 2388
Sample size of model......: 507
Missing data %............: 78.76884
Analysis of IL6_pg_ug_2015_LN.
- processing CalcificationPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + SmokerCurrent + CAD_history + stenose, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Age SmokerCurrentyes CAD_history
-1.44653 -0.07084 0.01994 0.39936 0.25473
stenose50-70% stenose70-90% stenose90-99% stenose100% (Occlusion) stenose50-99%
-1.00356 -0.50547 -0.26062 0.82473 -13.93339
stenose70-99%
-1.52574
Degrees of Freedom: 996 Total (i.e. Null); 986 Residual
Null Deviance: 1381
Residual Deviance: 1349 AIC: 1371
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.6142 -1.1333 -0.7968 1.1560 1.6718
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -1.129798 1.325225 -0.853 0.39392
currentDF[, PROTEIN] -0.069823 0.045164 -1.546 0.12211
Age 0.016974 0.008100 2.095 0.03613 *
Gendermale -0.133605 0.144147 -0.927 0.35400
Hypertension.compositeyes 0.232025 0.202513 1.146 0.25191
DiabetesStatusDiabetes -0.174736 0.159528 -1.095 0.27337
SmokerCurrentyes 0.414304 0.146715 2.824 0.00474 **
Med.Statin.LLDyes -0.164594 0.159158 -1.034 0.30106
Med.all.antiplateletyes -0.223848 0.217190 -1.031 0.30270
GFR_MDRD -0.001882 0.003492 -0.539 0.58999
BMI 0.012407 0.018096 0.686 0.49295
CAD_history 0.263345 0.149976 1.756 0.07910 .
Stroke_history -0.138179 0.140751 -0.982 0.32623
Peripheral.interv -0.183211 0.172892 -1.060 0.28929
stenose50-70% -0.937991 0.962569 -0.974 0.32983
stenose70-90% -0.484572 0.928921 -0.522 0.60191
stenose90-99% -0.235047 0.928443 -0.253 0.80014
stenose100% (Occlusion) 0.820707 1.245138 0.659 0.50981
stenose50-99% -14.005575 368.450051 -0.038 0.96968
stenose70-99% -1.450206 1.253664 -1.157 0.24736
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1381.3 on 996 degrees of freedom
Residual deviance: 1340.1 on 977 degrees of freedom
AIC: 1380.1
Number of Fisher Scoring iterations: 12
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_LN ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_pg_ug_2015_LN
Trait/outcome.............: CalcificationPlaque
Effect size...............: -0.069823
Standard error............: 0.045164
Odds ratio (effect size)..: 0.933
Lower 95% CI..............: 0.854
Upper 95% CI..............: 1.019
Z-value...................: -1.545965
P-value...................: 0.122113
Hosmer and Lemeshow r^2...: 0.029786
Cox and Snell r^2.........: 0.040427
Nagelkerke's pseudo r^2...: 0.053918
Sample size of AE DB......: 2388
Sample size of model......: 997
Missing data %............: 58.24958
- processing CollagenPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Hypertension.composite + SmokerCurrent + BMI + Stroke_history,
family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Hypertension.compositeyes SmokerCurrentyes BMI
-0.64730 -0.19549 0.33966 0.44766 0.03332
Stroke_history
0.25010
Degrees of Freedom: 999 Total (i.e. Null); 994 Residual
Null Deviance: 1017
Residual Deviance: 993.2 AIC: 1005
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.2738 0.4496 0.6096 0.7198 1.1160
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.232e+01 6.478e+02 0.019 0.984826
currentDF[, PROTEIN] -1.901e-01 5.643e-02 -3.370 0.000753 ***
Age 8.224e-03 9.876e-03 0.833 0.405022
Gendermale -8.624e-02 1.781e-01 -0.484 0.628235
Hypertension.compositeyes 3.110e-01 2.330e-01 1.335 0.181998
DiabetesStatusDiabetes 2.110e-01 2.038e-01 1.035 0.300511
SmokerCurrentyes 4.928e-01 1.863e-01 2.645 0.008165 **
Med.Statin.LLDyes -1.679e-02 1.955e-01 -0.086 0.931556
Med.all.antiplateletyes 1.321e-01 2.616e-01 0.505 0.613723
GFR_MDRD 4.699e-03 4.301e-03 1.092 0.274655
BMI 3.343e-02 2.363e-02 1.415 0.157050
CAD_history 2.203e-01 1.882e-01 1.171 0.241696
Stroke_history 2.401e-01 1.765e-01 1.360 0.173700
Peripheral.interv -1.730e-02 2.154e-01 -0.080 0.935980
stenose50-70% -1.361e+01 6.478e+02 -0.021 0.983245
stenose70-90% -1.401e+01 6.478e+02 -0.022 0.982741
stenose90-99% -1.406e+01 6.478e+02 -0.022 0.982686
stenose100% (Occlusion) 5.521e-01 8.166e+02 0.001 0.999461
stenose50-99% 1.725e-02 1.208e+03 0.000 0.999989
stenose70-99% -1.372e+01 6.478e+02 -0.021 0.983109
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1017.22 on 999 degrees of freedom
Residual deviance: 980.22 on 980 degrees of freedom
AIC: 1020.2
Number of Fisher Scoring iterations: 14
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_LN ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_pg_ug_2015_LN
Trait/outcome.............: CollagenPlaque
Effect size...............: -0.190149
Standard error............: 0.056431
Odds ratio (effect size)..: 0.827
Lower 95% CI..............: 0.74
Upper 95% CI..............: 0.924
Z-value...................: -3.369571
P-value...................: 0.000752854
Hosmer and Lemeshow r^2...: 0.03637
Cox and Snell r^2.........: 0.036321
Nagelkerke's pseudo r^2...: 0.056893
Sample size of AE DB......: 2388
Sample size of model......: 1000
Missing data %............: 58.12395
- processing Fat10Perc
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Gender + Stroke_history + Peripheral.interv, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Gendermale Stroke_history Peripheral.interv
1.5088 0.3120 0.8361 0.3705 -0.6128
Degrees of Freedom: 999 Total (i.e. Null); 995 Residual
Null Deviance: 1165
Residual Deviance: 1078 AIC: 1088
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.2376 -1.0048 0.6046 0.8035 2.0522
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.416e+01 3.908e+02 0.036 0.971101
currentDF[, PROTEIN] 3.150e-01 5.445e-02 5.785 7.25e-09 ***
Age 9.863e-03 9.331e-03 1.057 0.290516
Gendermale 8.540e-01 1.612e-01 5.299 1.16e-07 ***
Hypertension.compositeyes 6.925e-02 2.349e-01 0.295 0.768118
DiabetesStatusDiabetes -1.188e-01 1.840e-01 -0.646 0.518436
SmokerCurrentyes 9.721e-02 1.711e-01 0.568 0.569863
Med.Statin.LLDyes -1.762e-01 1.905e-01 -0.925 0.354945
Med.all.antiplateletyes 6.857e-02 2.500e-01 0.274 0.783889
GFR_MDRD -2.444e-04 4.099e-03 -0.060 0.952452
BMI 4.105e-03 2.044e-02 0.201 0.840876
CAD_history 8.625e-02 1.753e-01 0.492 0.622789
Stroke_history 3.758e-01 1.703e-01 2.207 0.027313 *
Peripheral.interv -6.179e-01 1.860e-01 -3.322 0.000895 ***
stenose50-70% -1.354e+01 3.908e+02 -0.035 0.972374
stenose70-90% -1.348e+01 3.908e+02 -0.035 0.972476
stenose90-99% -1.333e+01 3.908e+02 -0.034 0.972791
stenose100% (Occlusion) -1.432e+01 3.908e+02 -0.037 0.970764
stenose50-99% -1.499e+01 3.908e+02 -0.038 0.969397
stenose70-99% -1.463e+01 3.908e+02 -0.037 0.970143
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1164.5 on 999 degrees of freedom
Residual deviance: 1066.6 on 980 degrees of freedom
AIC: 1106.6
Number of Fisher Scoring iterations: 13
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_LN ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_pg_ug_2015_LN
Trait/outcome.............: Fat10Perc
Effect size...............: 0.314972
Standard error............: 0.054447
Odds ratio (effect size)..: 1.37
Lower 95% CI..............: 1.232
Upper 95% CI..............: 1.525
Z-value...................: 5.78494
P-value...................: 7.25384e-09
Hosmer and Lemeshow r^2...: 0.084067
Cox and Snell r^2.........: 0.093259
Nagelkerke's pseudo r^2...: 0.135564
Sample size of AE DB......: 2388
Sample size of model......: 1000
Missing data %............: 58.12395
- processing IPH
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Gender + Med.Statin.LLD +
BMI + CAD_history + Stroke_history + stenose, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) Gendermale Med.Statin.LLDyes BMI CAD_history
0.16028 0.59271 -0.25963 0.02923 0.30318
Stroke_history stenose50-70% stenose70-90% stenose90-99% stenose100% (Occlusion)
0.25028 -1.09743 -0.97086 -0.65159 -0.80087
stenose50-99% stenose70-99%
-15.27559 0.59126
Degrees of Freedom: 998 Total (i.e. Null); 987 Residual
Null Deviance: 1331
Residual Deviance: 1288 AIC: 1312
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.9361 -1.2697 0.8098 0.9793 1.4501
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.257533 1.493884 0.172 0.8631
currentDF[, PROTEIN] 0.039778 0.046572 0.854 0.3930
Age 0.001667 0.008309 0.201 0.8410
Gendermale 0.639327 0.146514 4.364 1.28e-05 ***
Hypertension.compositeyes -0.107679 0.207227 -0.520 0.6033
DiabetesStatusDiabetes -0.123508 0.163543 -0.755 0.4501
SmokerCurrentyes 0.124348 0.151574 0.820 0.4120
Med.Statin.LLDyes -0.249362 0.167276 -1.491 0.1360
Med.all.antiplateletyes 0.162196 0.221065 0.734 0.4631
GFR_MDRD -0.004962 0.003614 -1.373 0.1698
BMI 0.033097 0.018698 1.770 0.0767 .
CAD_history 0.311877 0.157241 1.983 0.0473 *
Stroke_history 0.230077 0.146495 1.571 0.1163
Peripheral.interv 0.037229 0.178257 0.209 0.8346
stenose50-70% -1.012619 1.165333 -0.869 0.3849
stenose70-90% -0.905980 1.139839 -0.795 0.4267
stenose90-99% -0.591956 1.139857 -0.519 0.6035
stenose100% (Occlusion) -0.746506 1.357852 -0.550 0.5825
stenose50-99% -15.241325 376.943340 -0.040 0.9677
stenose70-99% 0.596010 1.581074 0.377 0.7062
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1331.0 on 998 degrees of freedom
Residual deviance: 1283.3 on 979 degrees of freedom
AIC: 1323.3
Number of Fisher Scoring iterations: 12
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_LN ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_pg_ug_2015_LN
Trait/outcome.............: IPH
Effect size...............: 0.039778
Standard error............: 0.046572
Odds ratio (effect size)..: 1.041
Lower 95% CI..............: 0.95
Upper 95% CI..............: 1.14
Z-value...................: 0.85412
P-value...................: 0.3930386
Hosmer and Lemeshow r^2...: 0.035859
Cox and Snell r^2.........: 0.046653
Nagelkerke's pseudo r^2...: 0.063374
Sample size of AE DB......: 2388
Sample size of model......: 999
Missing data %............: 58.16583
Analysis of IL6R_pg_ug_2015_LN.
- processing CalcificationPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + Hypertension.composite +
DiabetesStatus + SmokerCurrent + CAD_history + stenose, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) Age Hypertension.compositeyes DiabetesStatusDiabetes SmokerCurrentyes
-0.46167 0.01611 0.29114 -0.24729 0.32686
CAD_history stenose50-70% stenose70-90% stenose90-99% stenose100% (Occlusion)
0.23621 -1.57544 -1.17320 -0.93369 0.08517
stenose50-99% stenose70-99%
-15.69463 -2.08381
Degrees of Freedom: 999 Total (i.e. Null); 988 Residual
Null Deviance: 1385
Residual Deviance: 1353 AIC: 1377
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.5698 -1.1312 -0.8315 1.1597 1.7348
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.176748 1.498941 0.118 0.9061
currentDF[, PROTEIN] -0.019047 0.057409 -0.332 0.7401
Age 0.012980 0.008150 1.593 0.1113
Gendermale -0.161581 0.144002 -1.122 0.2618
Hypertension.compositeyes 0.296356 0.200256 1.480 0.1389
DiabetesStatusDiabetes -0.255738 0.159973 -1.599 0.1099
SmokerCurrentyes 0.332542 0.145873 2.280 0.0226 *
Med.Statin.LLDyes -0.112715 0.160946 -0.700 0.4837
Med.all.antiplateletyes -0.254266 0.219349 -1.159 0.2464
GFR_MDRD -0.002446 0.003549 -0.689 0.4907
BMI 0.004983 0.018471 0.270 0.7873
CAD_history 0.257460 0.149355 1.724 0.0847 .
Stroke_history -0.156422 0.139963 -1.118 0.2637
Peripheral.interv -0.196360 0.172913 -1.136 0.2561
stenose50-70% -1.456245 1.195518 -1.218 0.2232
stenose70-90% -1.097761 1.168294 -0.940 0.3474
stenose90-99% -0.843707 1.168572 -0.722 0.4703
stenose100% (Occlusion) 0.133436 1.429817 0.093 0.9256
stenose50-99% -15.576906 435.896639 -0.036 0.9715
stenose70-99% -2.003314 1.437855 -1.393 0.1635
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1385.4 on 999 degrees of freedom
Residual deviance: 1346.1 on 980 degrees of freedom
AIC: 1386.1
Number of Fisher Scoring iterations: 13
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_LN ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6R_pg_ug_2015_LN
Trait/outcome.............: CalcificationPlaque
Effect size...............: -0.019047
Standard error............: 0.057409
Odds ratio (effect size)..: 0.981
Lower 95% CI..............: 0.877
Upper 95% CI..............: 1.098
Z-value...................: -0.331779
P-value...................: 0.7400562
Hosmer and Lemeshow r^2...: 0.028387
Cox and Snell r^2.........: 0.038564
Nagelkerke's pseudo r^2...: 0.051434
Sample size of AE DB......: 2388
Sample size of model......: 1000
Missing data %............: 58.12395
- processing CollagenPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ SmokerCurrent +
CAD_history, family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) SmokerCurrentyes CAD_history
1.1267 0.4109 0.3192
Degrees of Freedom: 1002 Total (i.e. Null); 1000 Residual
Null Deviance: 1019
Residual Deviance: 1010 AIC: 1016
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.1355 0.4944 0.6318 0.7184 0.9827
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.278e+01 7.194e+02 0.018 0.9858
currentDF[, PROTEIN] -1.366e-02 7.120e-02 -0.192 0.8478
Age 9.559e-03 9.927e-03 0.963 0.3356
Gendermale 5.029e-02 1.749e-01 0.287 0.7737
Hypertension.compositeyes 2.868e-01 2.303e-01 1.245 0.2131
DiabetesStatusDiabetes 1.921e-01 2.028e-01 0.947 0.3435
SmokerCurrentyes 4.760e-01 1.852e-01 2.570 0.0102 *
Med.Statin.LLDyes 3.601e-02 1.950e-01 0.185 0.8535
Med.all.antiplateletyes 2.336e-01 2.584e-01 0.904 0.3661
GFR_MDRD 4.087e-03 4.360e-03 0.937 0.3486
BMI 2.855e-02 2.336e-02 1.222 0.2218
CAD_history 2.842e-01 1.881e-01 1.510 0.1310
Stroke_history 2.282e-01 1.750e-01 1.304 0.1921
Peripheral.interv 7.360e-02 2.159e-01 0.341 0.7332
stenose50-70% -1.364e+01 7.194e+02 -0.019 0.9849
stenose70-90% -1.408e+01 7.194e+02 -0.020 0.9844
stenose90-99% -1.407e+01 7.194e+02 -0.020 0.9844
stenose100% (Occlusion) 3.431e-01 8.801e+02 0.000 0.9997
stenose50-99% -1.324e-01 1.020e+03 0.000 0.9999
stenose70-99% -1.371e+01 7.194e+02 -0.019 0.9848
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1018.60 on 1002 degrees of freedom
Residual deviance: 992.95 on 983 degrees of freedom
AIC: 1032.9
Number of Fisher Scoring iterations: 14
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_LN ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6R_pg_ug_2015_LN
Trait/outcome.............: CollagenPlaque
Effect size...............: -0.013662
Standard error............: 0.071202
Odds ratio (effect size)..: 0.986
Lower 95% CI..............: 0.858
Upper 95% CI..............: 1.134
Z-value...................: -0.191877
P-value...................: 0.8478382
Hosmer and Lemeshow r^2...: 0.025181
Cox and Snell r^2.........: 0.025248
Nagelkerke's pseudo r^2...: 0.039586
Sample size of AE DB......: 2388
Sample size of model......: 1003
Missing data %............: 57.99833
- processing Fat10Perc
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Gender + Stroke_history + Peripheral.interv, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Gendermale Stroke_history Peripheral.interv
0.6843 0.1101 0.7946 0.4350 -0.6186
Degrees of Freedom: 1002 Total (i.e. Null); 998 Residual
Null Deviance: 1170
Residual Deviance: 1121 AIC: 1131
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.0766 -1.1267 0.6579 0.7991 1.8865
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.351e+01 4.411e+02 0.031 0.97557
currentDF[, PROTEIN] 9.090e-02 6.341e-02 1.433 0.15172
Age 1.139e-02 9.258e-03 1.230 0.21871
Gendermale 8.404e-01 1.584e-01 5.306 1.12e-07 ***
Hypertension.compositeyes 1.693e-03 2.298e-01 0.007 0.99412
DiabetesStatusDiabetes -1.098e-01 1.802e-01 -0.609 0.54239
SmokerCurrentyes 1.529e-01 1.676e-01 0.913 0.36148
Med.Statin.LLDyes -1.484e-01 1.900e-01 -0.781 0.43479
Med.all.antiplateletyes 4.453e-02 2.492e-01 0.179 0.85821
GFR_MDRD -5.368e-05 4.111e-03 -0.013 0.98958
BMI -5.117e-03 2.076e-02 -0.246 0.80532
CAD_history -6.419e-02 1.697e-01 -0.378 0.70522
Stroke_history 4.326e-01 1.662e-01 2.602 0.00927 **
Peripheral.interv -5.951e-01 1.842e-01 -3.230 0.00124 **
stenose50-70% -1.358e+01 4.411e+02 -0.031 0.97544
stenose70-90% -1.350e+01 4.411e+02 -0.031 0.97559
stenose90-99% -1.338e+01 4.411e+02 -0.030 0.97580
stenose100% (Occlusion) -1.416e+01 4.411e+02 -0.032 0.97438
stenose50-99% -1.588e+01 4.411e+02 -0.036 0.97129
stenose70-99% -1.469e+01 4.411e+02 -0.033 0.97343
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1170.4 on 1002 degrees of freedom
Residual deviance: 1106.1 on 983 degrees of freedom
AIC: 1146.1
Number of Fisher Scoring iterations: 13
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_LN ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6R_pg_ug_2015_LN
Trait/outcome.............: Fat10Perc
Effect size...............: 0.090903
Standard error............: 0.063414
Odds ratio (effect size)..: 1.095
Lower 95% CI..............: 0.967
Upper 95% CI..............: 1.24
Z-value...................: 1.433489
P-value...................: 0.1517182
Hosmer and Lemeshow r^2...: 0.054893
Cox and Snell r^2.........: 0.062046
Nagelkerke's pseudo r^2...: 0.090095
Sample size of AE DB......: 2388
Sample size of model......: 1003
Missing data %............: 57.99833
- processing IPH
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Gender + GFR_MDRD + CAD_history, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Gendermale GFR_MDRD CAD_history
0.611874 0.124739 0.579682 -0.005085 0.226634
Degrees of Freedom: 1000 Total (i.e. Null); 996 Residual
Null Deviance: 1337
Residual Deviance: 1310 AIC: 1320
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.9524 -1.2779 0.8266 0.9913 1.4676
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.935176 1.526049 0.613 0.5400
currentDF[, PROTEIN] 0.113747 0.058657 1.939 0.0525 .
Age -0.001143 0.008376 -0.136 0.8915
Gendermale 0.610108 0.146043 4.178 2.95e-05 ***
Hypertension.compositeyes -0.106511 0.204153 -0.522 0.6019
DiabetesStatusDiabetes -0.149178 0.162470 -0.918 0.3585
SmokerCurrentyes 0.117770 0.150419 0.783 0.4337
Med.Statin.LLDyes -0.155812 0.167235 -0.932 0.3515
Med.all.antiplateletyes 0.059226 0.223727 0.265 0.7912
GFR_MDRD -0.005411 0.003670 -1.474 0.1404
BMI 0.017802 0.018962 0.939 0.3478
CAD_history 0.287268 0.155377 1.849 0.0645 .
Stroke_history 0.144300 0.144433 0.999 0.3178
Peripheral.interv 0.008765 0.178089 0.049 0.9607
stenose50-70% -0.738454 1.209037 -0.611 0.5413
stenose70-90% -0.767764 1.183606 -0.649 0.5166
stenose90-99% -0.518359 1.184171 -0.438 0.6616
stenose100% (Occlusion) -0.611519 1.393871 -0.439 0.6609
stenose50-99% -1.116618 1.557598 -0.717 0.4734
stenose70-99% 0.867308 1.609921 0.539 0.5901
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1336.7 on 1000 degrees of freedom
Residual deviance: 1298.9 on 981 degrees of freedom
AIC: 1338.9
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_LN ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6R_pg_ug_2015_LN
Trait/outcome.............: IPH
Effect size...............: 0.113747
Standard error............: 0.058657
Odds ratio (effect size)..: 1.12
Lower 95% CI..............: 0.999
Upper 95% CI..............: 1.257
Z-value...................: 1.93919
P-value...................: 0.05247816
Hosmer and Lemeshow r^2...: 0.028276
Cox and Snell r^2.........: 0.037054
Nagelkerke's pseudo r^2...: 0.050282
Sample size of AE DB......: 2388
Sample size of model......: 1001
Missing data %............: 58.08208
Analysis of MCP1_pg_ug_2015_LN.
- processing CalcificationPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + SmokerCurrent + CAD_history + stenose, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Age SmokerCurrentyes CAD_history
-1.82542 -0.36236 0.02094 0.40404 0.23730
stenose50-70% stenose70-90% stenose90-99% stenose100% (Occlusion) stenose50-99%
-0.89655 -0.37855 -0.19294 0.70403 -14.79800
stenose70-99%
-1.22893
Degrees of Freedom: 1038 Total (i.e. Null); 1028 Residual
Null Deviance: 1439
Residual Deviance: 1355 AIC: 1377
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.1140 -1.0778 -0.6941 1.1204 1.9531
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -1.525276 1.332747 -1.144 0.25243
currentDF[, PROTEIN] -0.363190 0.052973 -6.856 7.08e-12 ***
Age 0.017999 0.008173 2.202 0.02764 *
Gendermale -0.140301 0.144212 -0.973 0.33061
Hypertension.compositeyes 0.225902 0.202442 1.116 0.26447
DiabetesStatusDiabetes -0.244282 0.160978 -1.517 0.12914
SmokerCurrentyes 0.414717 0.147104 2.819 0.00481 **
Med.Statin.LLDyes -0.181849 0.161109 -1.129 0.25901
Med.all.antiplateletyes -0.202162 0.218816 -0.924 0.35555
GFR_MDRD -0.001691 0.003523 -0.480 0.63129
BMI 0.012186 0.018168 0.671 0.50237
CAD_history 0.260080 0.149984 1.734 0.08291 .
Stroke_history -0.113342 0.140901 -0.804 0.42116
Peripheral.interv -0.188752 0.172455 -1.094 0.27374
stenose50-70% -0.823566 0.967587 -0.851 0.39468
stenose70-90% -0.342758 0.932281 -0.368 0.71313
stenose90-99% -0.156801 0.931886 -0.168 0.86638
stenose100% (Occlusion) 0.711485 1.256594 0.566 0.57126
stenose50-99% -14.778796 419.945743 -0.035 0.97193
stenose70-99% -1.153193 1.267900 -0.910 0.36307
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1439.2 on 1038 degrees of freedom
Residual deviance: 1346.1 on 1019 degrees of freedom
AIC: 1386.1
Number of Fisher Scoring iterations: 13
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_LN ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_LN
Trait/outcome.............: CalcificationPlaque
Effect size...............: -0.36319
Standard error............: 0.052973
Odds ratio (effect size)..: 0.695
Lower 95% CI..............: 0.627
Upper 95% CI..............: 0.772
Z-value...................: -6.856102
P-value...................: 7.076467e-12
Hosmer and Lemeshow r^2...: 0.064659
Cox and Snell r^2.........: 0.085669
Nagelkerke's pseudo r^2...: 0.114268
Sample size of AE DB......: 2388
Sample size of model......: 1039
Missing data %............: 56.49079
- processing CollagenPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
SmokerCurrent + BMI, family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] SmokerCurrentyes BMI
-0.01029 -0.19213 0.44318 0.03858
Degrees of Freedom: 1041 Total (i.e. Null); 1038 Residual
Null Deviance: 1055
Residual Deviance: 1036 AIC: 1044
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.3056 0.4422 0.6185 0.7210 1.0417
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.324e+01 1.060e+03 0.012 0.99003
currentDF[, PROTEIN] -1.874e-01 6.331e-02 -2.961 0.00307 **
Age 1.043e-02 9.729e-03 1.072 0.28352
Gendermale 2.184e-02 1.728e-01 0.126 0.89942
Hypertension.compositeyes 2.349e-01 2.302e-01 1.020 0.30753
DiabetesStatusDiabetes 1.277e-01 1.982e-01 0.644 0.51942
SmokerCurrentyes 5.186e-01 1.837e-01 2.823 0.00475 **
Med.Statin.LLDyes 1.202e-02 1.924e-01 0.062 0.95021
Med.all.antiplateletyes 1.550e-01 2.563e-01 0.605 0.54545
GFR_MDRD 5.536e-03 4.245e-03 1.304 0.19220
BMI 4.039e-02 2.311e-02 1.748 0.08050 .
CAD_history 2.111e-01 1.839e-01 1.148 0.25100
Stroke_history 2.243e-01 1.728e-01 1.298 0.19424
Peripheral.interv 1.123e-01 2.127e-01 0.528 0.59749
stenose50-70% -1.453e+01 1.060e+03 -0.014 0.98906
stenose70-90% -1.499e+01 1.060e+03 -0.014 0.98872
stenose90-99% -1.505e+01 1.060e+03 -0.014 0.98867
stenose100% (Occlusion) 2.360e-01 1.350e+03 0.000 0.99986
stenose50-99% -6.401e-03 1.588e+03 0.000 1.00000
stenose70-99% -1.448e+01 1.060e+03 -0.014 0.98910
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1055.5 on 1041 degrees of freedom
Residual deviance: 1018.8 on 1022 degrees of freedom
AIC: 1058.8
Number of Fisher Scoring iterations: 15
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_LN ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_LN
Trait/outcome.............: CollagenPlaque
Effect size...............: -0.187427
Standard error............: 0.063307
Odds ratio (effect size)..: 0.829
Lower 95% CI..............: 0.732
Upper 95% CI..............: 0.939
Z-value...................: -2.960631
P-value...................: 0.003070098
Hosmer and Lemeshow r^2...: 0.034718
Cox and Snell r^2.........: 0.034555
Nagelkerke's pseudo r^2...: 0.05426
Sample size of AE DB......: 2388
Sample size of model......: 1042
Missing data %............: 56.36516
- processing Fat10Perc
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Gender + Stroke_history + Peripheral.interv + stenose, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Gendermale Stroke_history Peripheral.interv
14.0651 0.1331 0.8322 0.4430 -0.6200
stenose50-70% stenose70-90% stenose90-99% stenose100% (Occlusion) stenose50-99%
-13.6515 -13.5243 -13.3644 -14.0689 -15.9382
stenose70-99%
-14.7683
Degrees of Freedom: 1041 Total (i.e. Null); 1031 Residual
Null Deviance: 1225
Residual Deviance: 1154 AIC: 1176
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.0991 -1.1400 0.6479 0.8098 1.7713
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.339e+01 3.919e+02 0.034 0.972738
currentDF[, PROTEIN] 1.285e-01 5.518e-02 2.328 0.019925 *
Age 7.929e-03 8.973e-03 0.884 0.376898
Gendermale 8.698e-01 1.541e-01 5.643 1.67e-08 ***
Hypertension.compositeyes 4.466e-02 2.266e-01 0.197 0.843736
DiabetesStatusDiabetes -1.596e-01 1.756e-01 -0.909 0.363550
SmokerCurrentyes 1.456e-01 1.637e-01 0.889 0.373964
Med.Statin.LLDyes -1.853e-01 1.862e-01 -0.995 0.319583
Med.all.antiplateletyes 8.146e-02 2.423e-01 0.336 0.736740
GFR_MDRD -5.399e-04 3.928e-03 -0.137 0.890678
BMI 4.117e-03 1.973e-02 0.209 0.834748
CAD_history -3.684e-02 1.661e-01 -0.222 0.824449
Stroke_history 4.317e-01 1.633e-01 2.643 0.008213 **
Peripheral.interv -5.934e-01 1.791e-01 -3.313 0.000923 ***
stenose50-70% -1.358e+01 3.919e+02 -0.035 0.972348
stenose70-90% -1.346e+01 3.919e+02 -0.034 0.972607
stenose90-99% -1.331e+01 3.919e+02 -0.034 0.972907
stenose100% (Occlusion) -1.405e+01 3.919e+02 -0.036 0.971411
stenose50-99% -1.592e+01 3.919e+02 -0.041 0.967601
stenose70-99% -1.478e+01 3.919e+02 -0.038 0.969917
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1224.7 on 1041 degrees of freedom
Residual deviance: 1150.0 on 1022 degrees of freedom
AIC: 1190
Number of Fisher Scoring iterations: 13
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_LN ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_LN
Trait/outcome.............: Fat10Perc
Effect size...............: 0.128452
Standard error............: 0.055183
Odds ratio (effect size)..: 1.137
Lower 95% CI..............: 1.021
Upper 95% CI..............: 1.267
Z-value...................: 2.327761
P-value...................: 0.01992479
Hosmer and Lemeshow r^2...: 0.060989
Cox and Snell r^2.........: 0.069173
Nagelkerke's pseudo r^2...: 0.100065
Sample size of AE DB......: 2388
Sample size of model......: 1042
Missing data %............: 56.36516
- processing IPH
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Gender + Med.Statin.LLD + BMI + CAD_history + Stroke_history,
family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Gendermale Med.Statin.LLDyes BMI CAD_history
-0.68313 -0.09602 0.54823 -0.26886 0.02693 0.26525
Stroke_history
0.23930
Degrees of Freedom: 1039 Total (i.e. Null); 1033 Residual
Null Deviance: 1389
Residual Deviance: 1358 AIC: 1372
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.9553 -1.2805 0.8149 0.9916 1.5235
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.1754155 1.4836591 0.118 0.9059
currentDF[, PROTEIN] -0.0953309 0.0507963 -1.877 0.0606 .
Age 0.0002349 0.0081421 0.029 0.9770
Gendermale 0.6158358 0.1429276 4.309 1.64e-05 ***
Hypertension.compositeyes -0.1314574 0.2022681 -0.650 0.5157
DiabetesStatusDiabetes -0.1324652 0.1596057 -0.830 0.4066
SmokerCurrentyes 0.1353568 0.1475791 0.917 0.3590
Med.Statin.LLDyes -0.2565402 0.1644513 -1.560 0.1188
Med.all.antiplateletyes 0.1480708 0.2179980 0.679 0.4970
GFR_MDRD -0.0049268 0.0035325 -1.395 0.1631
BMI 0.0315725 0.0183189 1.723 0.0848 .
CAD_history 0.2686674 0.1520378 1.767 0.0772 .
Stroke_history 0.2381253 0.1426755 1.669 0.0951 .
Peripheral.interv 0.0540398 0.1736594 0.311 0.7557
stenose50-70% -0.9864070 1.1701913 -0.843 0.3993
stenose70-90% -0.8577626 1.1446041 -0.749 0.4536
stenose90-99% -0.6037415 1.1445931 -0.527 0.5979
stenose100% (Occlusion) -0.7476970 1.3616375 -0.549 0.5829
stenose50-99% -1.2390402 1.5273473 -0.811 0.4172
stenose70-99% 0.6388947 1.5823481 0.404 0.6864
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1388.6 on 1039 degrees of freedom
Residual deviance: 1345.2 on 1020 degrees of freedom
AIC: 1385.2
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_LN ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_LN
Trait/outcome.............: IPH
Effect size...............: -0.095331
Standard error............: 0.050796
Odds ratio (effect size)..: 0.909
Lower 95% CI..............: 0.823
Upper 95% CI..............: 1.004
Z-value...................: -1.876731
P-value...................: 0.06055499
Hosmer and Lemeshow r^2...: 0.031257
Cox and Snell r^2.........: 0.040876
Nagelkerke's pseudo r^2...: 0.05547
Sample size of AE DB......: 2388
Sample size of model......: 1040
Missing data %............: 56.44891 cat("Edit the column names...\n")Edit the column names...colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
"Beta", "s.e.m.",
"OR", "low95CI", "up95CI",
"Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")
cat("Correct the variable types...\n")Correct the variable types...GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)
# Save the data
cat("Writing results to Excel-file...\n")Writing results to Excel-file...### Univariate
write.xlsx(GLM.results,
file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Multi.Protein.PlaquePhenotypes.MODEL2.xlsx"),
row.names = FALSE, col.names = TRUE, sheetName = "Bin.Multi.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")Removing intermediate files...rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)
Inverse-rank normalization
Here we use the inverse-rank normalized data - visually this is more normally distributed.
Analysis of continuous/quantitative plaque traits as a function of serum/plaque IL6(R)/MCP1 levels.
GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")Running linear regression...for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
PROTEIN = TRAITS.PROTEIN.RANK[protein]
cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
for (trait in 1:length(TRAITS.CON.RANK)) {
TRAIT = TRAITS.CON.RANK[trait]
cat(paste0("\n- processing ",TRAIT,"\n\n"))
currentDF <- as.data.frame(AEDB.CEA %>%
dplyr::select(., PROTEIN, TRAIT, COVARIATES_M2) %>%
filter(complete.cases(.))) %>%
filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
# for debug
# print(DT::datatable(currentDF))
# print(nrow(currentDF))
# print(str(currentDF))
### univariate
fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender +
Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose,
data = currentDF)
model_step <- stepAIC(fit, direction = "both", trace = FALSE)
print(model_step)
print(summary(fit))
GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
GLM.results.TEMP[1,] = GLM.CON(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
GLM.results = rbind(GLM.results, GLM.results.TEMP)
}
}
Analysis of IL6_rank.
- processing Macrophages_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Med.Statin.LLD +
CAD_history, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Med.Statin.LLDyes CAD_history
0.06578 -0.06829 -0.15943 0.17897
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose,
data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.18012 -0.68054 0.01814 0.64545 3.12461
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.0235458 0.8716104 2.322 0.0207 *
currentDF[, TRAIT] -0.0685416 0.0417359 -1.642 0.1012
Age -0.0066245 0.0057643 -1.149 0.2510
Gendermale -0.1469518 0.1015109 -1.448 0.1484
Hypertension.compositeyes 0.0462797 0.1352200 0.342 0.7323
DiabetesStatusDiabetes -0.0287032 0.1144777 -0.251 0.8021
SmokerCurrentyes 0.0296470 0.0980327 0.302 0.7625
Med.Statin.LLDyes -0.1940609 0.1023977 -1.895 0.0587 .
Med.all.antiplateletyes -0.2470769 0.1608439 -1.536 0.1252
GFR_MDRD 0.0005454 0.0025737 0.212 0.8323
BMI -0.0165307 0.0123925 -1.334 0.1829
CAD_history 0.2248719 0.1011374 2.223 0.0267 *
Stroke_history 0.0695885 0.0961747 0.724 0.4697
Peripheral.interv -0.0107614 0.1147908 -0.094 0.9254
stenose50-70% -0.8489816 0.6177818 -1.374 0.1700
stenose70-90% -0.8371936 0.5706953 -1.467 0.1431
stenose90-99% -0.8337094 0.5690414 -1.465 0.1436
stenose100% (Occlusion) -1.3089080 0.6810469 -1.922 0.0552 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9653 on 462 degrees of freedom
Multiple R-squared: 0.03962, Adjusted R-squared: 0.004284
F-statistic: 1.121 on 17 and 462 DF, p-value: 0.3297
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_rank ' with ' Macrophages_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_rank
Trait/outcome.............: Macrophages_rank
Effect size...............: -0.068542
Standard error............: 0.041736
Odds ratio (effect size)..: 0.934
Lower 95% CI..............: 0.86
Upper 95% CI..............: 1.013
T-value...................: -1.64227
P-value...................: 0.1012144
R^2.......................: 0.039622
Adjusted r^2..............: 0.004284
Sample size of AE DB......: 2388
Sample size of model......: 480
Missing data %............: 79.8995
- processing SMC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Gender + Med.Statin.LLD +
BMI + CAD_history, data = currentDF)
Coefficients:
(Intercept) Gendermale Med.Statin.LLDyes BMI CAD_history
0.62797 -0.14791 -0.17078 -0.01678 0.18459
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose,
data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.10464 -0.69874 0.03157 0.67237 3.02975
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.0177233 0.8817683 2.288 0.0226 *
currentDF[, TRAIT] 0.0247937 0.0442872 0.560 0.5759
Age -0.0062426 0.0059358 -1.052 0.2935
Gendermale -0.1471284 0.1039038 -1.416 0.1575
Hypertension.compositeyes 0.0194356 0.1356093 0.143 0.8861
DiabetesStatusDiabetes -0.0182235 0.1157824 -0.157 0.8750
SmokerCurrentyes 0.0278929 0.0989731 0.282 0.7782
Med.Statin.LLDyes -0.1848209 0.1040135 -1.777 0.0762 .
Med.all.antiplateletyes -0.2376107 0.1622751 -1.464 0.1438
GFR_MDRD 0.0005086 0.0026001 0.196 0.8450
BMI -0.0174703 0.0124984 -1.398 0.1628
CAD_history 0.1915058 0.1021081 1.876 0.0614 .
Stroke_history 0.0570212 0.0971729 0.587 0.5576
Peripheral.interv 0.0059065 0.1162497 0.051 0.9595
stenose50-70% -0.8228165 0.6236546 -1.319 0.1877
stenose70-90% -0.8299597 0.5757742 -1.441 0.1501
stenose90-99% -0.7914493 0.5736687 -1.380 0.1684
stenose100% (Occlusion) -1.2535737 0.6865446 -1.826 0.0685 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9735 on 459 degrees of freedom
Multiple R-squared: 0.03312, Adjusted R-squared: -0.002686
F-statistic: 0.925 on 17 and 459 DF, p-value: 0.5443
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_rank ' with ' SMC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_rank
Trait/outcome.............: SMC_rank
Effect size...............: 0.024794
Standard error............: 0.044287
Odds ratio (effect size)..: 1.025
Lower 95% CI..............: 0.94
Upper 95% CI..............: 1.118
T-value...................: 0.55984
P-value...................: 0.5758615
R^2.......................: 0.033124
Adjusted r^2..............: -0.002686
Sample size of AE DB......: 2388
Sample size of model......: 477
Missing data %............: 80.02513
- processing VesselDensity_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Med.Statin.LLD + Med.all.antiplatelet +
CAD_history, data = currentDF)
Coefficients:
(Intercept) Med.Statin.LLDyes Med.all.antiplateletyes CAD_history
0.2792 -0.1532 -0.2243 0.1419
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose,
data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.09803 -0.68750 0.01001 0.67790 3.09629
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.9561136 0.8855768 2.209 0.0277 *
currentDF[, TRAIT] -0.0323443 0.0567070 -0.570 0.5687
Age -0.0071652 0.0058979 -1.215 0.2250
Gendermale -0.1413460 0.1036164 -1.364 0.1732
Hypertension.compositeyes 0.0500332 0.1385484 0.361 0.7182
DiabetesStatusDiabetes -0.0297531 0.1175472 -0.253 0.8003
SmokerCurrentyes 0.0287580 0.0998963 0.288 0.7736
Med.Statin.LLDyes -0.1881625 0.1043566 -1.803 0.0720 .
Med.all.antiplateletyes -0.2581952 0.1641560 -1.573 0.1164
GFR_MDRD 0.0006495 0.0026302 0.247 0.8051
BMI -0.0144740 0.0127453 -1.136 0.2567
CAD_history 0.1948838 0.1024046 1.903 0.0577 .
Stroke_history 0.0843472 0.0983333 0.858 0.3915
Peripheral.interv 0.0036368 0.1168602 0.031 0.9752
stenose50-70% -0.7800714 0.6238008 -1.251 0.2118
stenose70-90% -0.8067160 0.5767651 -1.399 0.1626
stenose90-99% -0.7793910 0.5748524 -1.356 0.1758
stenose100% (Occlusion) -0.9176088 0.7281714 -1.260 0.2083
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9754 on 452 degrees of freedom
Multiple R-squared: 0.03081, Adjusted R-squared: -0.005638
F-statistic: 0.8453 on 17 and 452 DF, p-value: 0.64
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_rank ' with ' VesselDensity_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_rank
Trait/outcome.............: VesselDensity_rank
Effect size...............: -0.032344
Standard error............: 0.056707
Odds ratio (effect size)..: 0.968
Lower 95% CI..............: 0.866
Upper 95% CI..............: 1.082
T-value...................: -0.570376
P-value...................: 0.5687063
R^2.......................: 0.030814
Adjusted r^2..............: -0.005638
Sample size of AE DB......: 2388
Sample size of model......: 470
Missing data %............: 80.31826
Analysis of MCP1_rank.
- processing Macrophages_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + Med.Statin.LLD + Med.all.antiplatelet,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age Gendermale Hypertension.compositeyes
0.371369 0.098495 -0.007782 0.304434 -0.224359
Med.Statin.LLDyes Med.all.antiplateletyes
-0.214084 0.325933
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose,
data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.04992 -0.67471 0.01276 0.66488 2.76675
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.175752 0.858226 1.370 0.17131
currentDF[, TRAIT] 0.091886 0.040985 2.242 0.02541 *
Age -0.011585 0.005692 -2.035 0.04237 *
Gendermale 0.290308 0.098872 2.936 0.00348 **
Hypertension.compositeyes -0.233518 0.132648 -1.760 0.07895 .
DiabetesStatusDiabetes -0.121623 0.110871 -1.097 0.27318
SmokerCurrentyes -0.062330 0.095859 -0.650 0.51585
Med.Statin.LLDyes -0.220521 0.101730 -2.168 0.03066 *
Med.all.antiplateletyes 0.301211 0.154422 1.951 0.05167 .
GFR_MDRD -0.001676 0.002477 -0.677 0.49899
BMI -0.012215 0.011885 -1.028 0.30455
CAD_history 0.110540 0.099554 1.110 0.26739
Stroke_history 0.115056 0.093287 1.233 0.21803
Peripheral.interv -0.150917 0.117008 -1.290 0.19772
stenose50-70% -0.280324 0.622960 -0.450 0.65292
stenose70-90% -0.054810 0.578053 -0.095 0.92450
stenose90-99% -0.042985 0.576571 -0.075 0.94060
stenose100% (Occlusion) -0.487945 0.729821 -0.669 0.50407
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9798 on 495 degrees of freedom
Multiple R-squared: 0.07266, Adjusted R-squared: 0.04081
F-statistic: 2.282 on 17 and 495 DF, p-value: 0.002535
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' Macrophages_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: Macrophages_rank
Effect size...............: 0.091886
Standard error............: 0.040985
Odds ratio (effect size)..: 1.096
Lower 95% CI..............: 1.012
Upper 95% CI..............: 1.188
T-value...................: 2.241938
P-value...................: 0.02540749
R^2.......................: 0.072662
Adjusted r^2..............: 0.040814
Sample size of AE DB......: 2388
Sample size of model......: 513
Missing data %............: 78.51759
- processing SMC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + Med.Statin.LLD + Med.all.antiplatelet +
CAD_history, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age Gendermale Hypertension.compositeyes
0.79555 -0.18754 -0.01327 0.22987 -0.22144
Med.Statin.LLDyes Med.all.antiplateletyes CAD_history
-0.22763 0.32134 0.13508
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose,
data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.82157 -0.67164 -0.01232 0.64680 2.51654
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.516767 0.843745 1.798 0.07284 .
currentDF[, TRAIT] -0.184221 0.041848 -4.402 1.32e-05 ***
Age -0.016216 0.005685 -2.853 0.00452 **
Gendermale 0.227577 0.098012 2.322 0.02064 *
Hypertension.compositeyes -0.217011 0.129139 -1.680 0.09351 .
DiabetesStatusDiabetes -0.111461 0.108990 -1.023 0.30697
SmokerCurrentyes -0.064436 0.093923 -0.686 0.49300
Med.Statin.LLDyes -0.226750 0.100142 -2.264 0.02399 *
Med.all.antiplateletyes 0.292797 0.151249 1.936 0.05346 .
GFR_MDRD -0.001337 0.002428 -0.551 0.58211
BMI -0.011722 0.011633 -1.008 0.31411
CAD_history 0.166154 0.097613 1.702 0.08935 .
Stroke_history 0.113839 0.091470 1.245 0.21389
Peripheral.interv -0.131918 0.115289 -1.144 0.25308
stenose50-70% -0.246421 0.610039 -0.404 0.68643
stenose70-90% -0.055765 0.566182 -0.098 0.92158
stenose90-99% -0.067374 0.564421 -0.119 0.90503
stenose100% (Occlusion) -0.599452 0.714404 -0.839 0.40182
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9594 on 492 degrees of freedom
Multiple R-squared: 0.09563, Adjusted R-squared: 0.06438
F-statistic: 3.06 on 17 and 492 DF, p-value: 3.937e-05
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' SMC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: SMC_rank
Effect size...............: -0.184221
Standard error............: 0.041848
Odds ratio (effect size)..: 0.832
Lower 95% CI..............: 0.766
Upper 95% CI..............: 0.903
T-value...................: -4.402188
P-value...................: 1.315371e-05
R^2.......................: 0.095625
Adjusted r^2..............: 0.064376
Sample size of AE DB......: 2388
Sample size of model......: 510
Missing data %............: 78.64322
- processing VesselDensity_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Med.Statin.LLD + Med.all.antiplatelet, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age Gendermale Med.Statin.LLDyes
0.30680 -0.09313 -0.00927 0.32989 -0.22147
Med.all.antiplateletyes
0.32424
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose,
data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.08723 -0.66290 -0.00982 0.64415 2.61016
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.081798 0.865574 1.250 0.21197
currentDF[, TRAIT] -0.098755 0.054234 -1.821 0.06924 .
Age -0.012056 0.005761 -2.093 0.03690 *
Gendermale 0.311368 0.100077 3.111 0.00197 **
Hypertension.compositeyes -0.171260 0.135231 -1.266 0.20597
DiabetesStatusDiabetes -0.094615 0.112880 -0.838 0.40234
SmokerCurrentyes -0.067259 0.097251 -0.692 0.48951
Med.Statin.LLDyes -0.226120 0.102933 -2.197 0.02851 *
Med.all.antiplateletyes 0.297676 0.156807 1.898 0.05824 .
GFR_MDRD -0.001178 0.002544 -0.463 0.64359
BMI -0.009717 0.012040 -0.807 0.42001
CAD_history 0.136143 0.100924 1.349 0.17798
Stroke_history 0.120457 0.094866 1.270 0.20478
Peripheral.interv -0.133039 0.118703 -1.121 0.26294
stenose50-70% -0.359473 0.625133 -0.575 0.56553
stenose70-90% -0.053904 0.580427 -0.093 0.92605
stenose90-99% -0.062723 0.578764 -0.108 0.91374
stenose100% (Occlusion) -0.550773 0.732645 -0.752 0.45256
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9835 on 485 degrees of freedom
Multiple R-squared: 0.06824, Adjusted R-squared: 0.03558
F-statistic: 2.089 on 17 and 485 DF, p-value: 0.006596
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' VesselDensity_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: VesselDensity_rank
Effect size...............: -0.098755
Standard error............: 0.054234
Odds ratio (effect size)..: 0.906
Lower 95% CI..............: 0.815
Upper 95% CI..............: 1.008
T-value...................: -1.820907
P-value...................: 0.06923707
R^2.......................: 0.068242
Adjusted r^2..............: 0.035582
Sample size of AE DB......: 2388
Sample size of model......: 503
Missing data %............: 78.93635
Analysis of IL6_pg_ug_2015_rank.
- processing Macrophages_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + CAD_history +
Stroke_history + Peripheral.interv, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] CAD_history Stroke_history Peripheral.interv
0.01886 0.09601 -0.14506 0.18452 -0.14721
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose,
data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.1909 -0.6764 0.0049 0.6426 3.4119
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.598104 0.634370 0.943 0.34600
currentDF[, TRAIT] 0.093832 0.031754 2.955 0.00320 **
Age -0.003879 0.003906 -0.993 0.32090
Gendermale 0.009744 0.069831 0.140 0.88906
Hypertension.compositeyes -0.100809 0.097138 -1.038 0.29962
DiabetesStatusDiabetes -0.008597 0.076764 -0.112 0.91086
SmokerCurrentyes 0.064296 0.070471 0.912 0.36179
Med.Statin.LLDyes -0.064710 0.076835 -0.842 0.39988
Med.all.antiplateletyes 0.011430 0.104312 0.110 0.91277
GFR_MDRD -0.002656 0.001676 -1.585 0.11323
BMI -0.008334 0.008727 -0.955 0.33981
CAD_history -0.108752 0.072191 -1.506 0.13228
Stroke_history 0.188239 0.067608 2.784 0.00547 **
Peripheral.interv -0.149582 0.083267 -1.796 0.07274 .
stenose50-70% -0.037014 0.459362 -0.081 0.93579
stenose70-90% 0.226650 0.444258 0.510 0.61004
stenose90-99% 0.173795 0.444035 0.391 0.69559
stenose100% (Occlusion) 0.562595 0.564475 0.997 0.31917
stenose50-99% 0.021481 0.824910 0.026 0.97923
stenose70-99% -0.186502 0.598315 -0.312 0.75533
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.984 on 976 degrees of freedom
Multiple R-squared: 0.04085, Adjusted R-squared: 0.02218
F-statistic: 2.188 on 19 and 976 DF, p-value: 0.002371
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_rank ' with ' Macrophages_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_pg_ug_2015_rank
Trait/outcome.............: Macrophages_rank
Effect size...............: 0.093832
Standard error............: 0.031754
Odds ratio (effect size)..: 1.098
Lower 95% CI..............: 1.032
Upper 95% CI..............: 1.169
T-value...................: 2.954983
P-value...................: 0.003201987
R^2.......................: 0.040848
Adjusted r^2..............: 0.022176
Sample size of AE DB......: 2388
Sample size of model......: 996
Missing data %............: 58.29146
- processing SMC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
CAD_history + Stroke_history + Peripheral.interv, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age CAD_history Stroke_history Peripheral.interv
0.510465 -0.157509 -0.007324 -0.123126 0.197685 -0.159919
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose,
data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.93275 -0.66240 0.01907 0.64346 2.96767
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.786671 0.630026 1.249 0.21210
currentDF[, TRAIT] -0.162944 0.032868 -4.958 8.42e-07 ***
Age -0.008232 0.003918 -2.101 0.03587 *
Gendermale -0.033556 0.070213 -0.478 0.63282
Hypertension.compositeyes -0.086195 0.096425 -0.894 0.37159
DiabetesStatusDiabetes -0.004985 0.076199 -0.065 0.94785
SmokerCurrentyes 0.060184 0.069997 0.860 0.39011
Med.Statin.LLDyes -0.065859 0.076396 -0.862 0.38886
Med.all.antiplateletyes -0.016292 0.103511 -0.157 0.87497
GFR_MDRD -0.002233 0.001666 -1.341 0.18034
BMI -0.007551 0.008672 -0.871 0.38410
CAD_history -0.090057 0.071752 -1.255 0.20974
Stroke_history 0.197606 0.067061 2.947 0.00329 **
Peripheral.interv -0.161279 0.083037 -1.942 0.05240 .
stenose50-70% 0.023861 0.455875 0.052 0.95827
stenose70-90% 0.318075 0.440924 0.721 0.47085
stenose90-99% 0.277050 0.440793 0.629 0.52981
stenose100% (Occlusion) 0.626538 0.560171 1.118 0.26364
stenose50-99% 0.198613 0.819108 0.242 0.80846
stenose70-99% -0.083839 0.593759 -0.141 0.88774
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9761 on 972 degrees of freedom
Multiple R-squared: 0.05658, Adjusted R-squared: 0.03814
F-statistic: 3.068 on 19 and 972 DF, p-value: 1.101e-05
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_rank ' with ' SMC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_pg_ug_2015_rank
Trait/outcome.............: SMC_rank
Effect size...............: -0.162944
Standard error............: 0.032868
Odds ratio (effect size)..: 0.85
Lower 95% CI..............: 0.797
Upper 95% CI..............: 0.906
T-value...................: -4.957502
P-value...................: 8.422128e-07
R^2.......................: 0.056578
Adjusted r^2..............: 0.038137
Sample size of AE DB......: 2388
Sample size of model......: 992
Missing data %............: 58.45896
- processing VesselDensity_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + SmokerCurrent +
GFR_MDRD + CAD_history + Stroke_history + Peripheral.interv,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] SmokerCurrentyes GFR_MDRD CAD_history Stroke_history
0.146966 -0.066091 0.105951 -0.002514 -0.129845 0.190585
Peripheral.interv
-0.173758
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose,
data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.1508 -0.6383 0.0036 0.6501 3.2094
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.665290 0.649313 1.025 0.30582
currentDF[, TRAIT] -0.068097 0.033572 -2.028 0.04281 *
Age -0.004320 0.004054 -1.066 0.28681
Gendermale 0.003466 0.072569 0.048 0.96192
Hypertension.compositeyes -0.106848 0.100976 -1.058 0.29026
DiabetesStatusDiabetes -0.050711 0.081619 -0.621 0.53454
SmokerCurrentyes 0.062220 0.073689 0.844 0.39869
Med.Statin.LLDyes -0.055150 0.079291 -0.696 0.48690
Med.all.antiplateletyes -0.017531 0.109861 -0.160 0.87325
GFR_MDRD -0.003260 0.001759 -1.853 0.06418 .
BMI -0.005782 0.009030 -0.640 0.52214
CAD_history -0.094898 0.075609 -1.255 0.20976
Stroke_history 0.207070 0.070464 2.939 0.00338 **
Peripheral.interv -0.166293 0.088420 -1.881 0.06033 .
stenose50-70% -0.128577 0.465413 -0.276 0.78241
stenose70-90% 0.179173 0.448315 0.400 0.68950
stenose90-99% 0.140252 0.447728 0.313 0.75416
stenose100% (Occlusion) 0.489070 0.569196 0.859 0.39044
stenose50-99% 0.015460 0.830783 0.019 0.98516
stenose70-99% -0.455543 0.667542 -0.682 0.49515
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9909 on 910 degrees of freedom
Multiple R-squared: 0.03919, Adjusted R-squared: 0.01913
F-statistic: 1.953 on 19 and 910 DF, p-value: 0.008611
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_rank ' with ' VesselDensity_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_pg_ug_2015_rank
Trait/outcome.............: VesselDensity_rank
Effect size...............: -0.068097
Standard error............: 0.033572
Odds ratio (effect size)..: 0.934
Lower 95% CI..............: 0.875
Upper 95% CI..............: 0.998
T-value...................: -2.02839
P-value...................: 0.04281124
R^2.......................: 0.039187
Adjusted r^2..............: 0.019126
Sample size of AE DB......: 2388
Sample size of model......: 930
Missing data %............: 61.05528
Analysis of IL6R_pg_ug_2015_rank.
- processing Macrophages_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Med.Statin.LLD + GFR_MDRD + CAD_history + Peripheral.interv +
stenose, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age Med.Statin.LLDyes GFR_MDRD
0.254402 0.138291 -0.008368 -0.371568 -0.003134
CAD_history Peripheral.interv stenose50-70% stenose70-90% stenose90-99%
-0.099103 0.233323 0.518699 0.800413 0.942022
stenose100% (Occlusion) stenose50-99% stenose70-99%
0.655301 0.565404 -0.108615
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose,
data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.1926 -0.6496 -0.0011 0.6198 3.0610
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.274225 0.663148 0.414 0.67932
currentDF[, TRAIT] 0.139450 0.030997 4.499 7.65e-06 ***
Age -0.007934 0.003880 -2.045 0.04117 *
Gendermale -0.058157 0.068901 -0.844 0.39884
Hypertension.compositeyes 0.055508 0.095193 0.583 0.55996
DiabetesStatusDiabetes -0.077646 0.075649 -1.026 0.30496
SmokerCurrentyes 0.059294 0.069374 0.855 0.39293
Med.Statin.LLDyes -0.373281 0.076205 -4.898 1.13e-06 ***
Med.all.antiplateletyes 0.038778 0.103983 0.373 0.70928
GFR_MDRD -0.003053 0.001682 -1.815 0.06981 .
BMI -0.005349 0.008797 -0.608 0.54328
CAD_history -0.074440 0.070919 -1.050 0.29414
Stroke_history 0.067980 0.066451 1.023 0.30656
Peripheral.interv 0.233457 0.082462 2.831 0.00473 **
stenose50-70% 0.533224 0.503840 1.058 0.29017
stenose70-90% 0.814412 0.489795 1.663 0.09668 .
stenose90-99% 0.950282 0.489612 1.941 0.05256 .
stenose100% (Occlusion) 0.670810 0.598031 1.122 0.26227
stenose50-99% 0.538437 0.687643 0.783 0.43381
stenose70-99% -0.113878 0.629316 -0.181 0.85644
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9703 on 979 degrees of freedom
Multiple R-squared: 0.08311, Adjusted R-squared: 0.06531
F-statistic: 4.67 on 19 and 979 DF, p-value: 1.919e-10
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_rank ' with ' Macrophages_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6R_pg_ug_2015_rank
Trait/outcome.............: Macrophages_rank
Effect size...............: 0.13945
Standard error............: 0.030997
Odds ratio (effect size)..: 1.15
Lower 95% CI..............: 1.082
Upper 95% CI..............: 1.222
T-value...................: 4.498795
P-value...................: 7.653324e-06
R^2.......................: 0.083108
Adjusted r^2..............: 0.065313
Sample size of AE DB......: 2388
Sample size of model......: 999
Missing data %............: 58.16583
- processing SMC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Med.Statin.LLD + GFR_MDRD + Stroke_history + Peripheral.interv +
stenose, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age Med.Statin.LLDyes GFR_MDRD
0.190698 0.062823 -0.008893 -0.373218 -0.002728
Stroke_history Peripheral.interv stenose50-70% stenose70-90% stenose90-99%
0.096206 0.210013 0.503849 0.820847 0.949222
stenose100% (Occlusion) stenose50-99% stenose70-99%
0.641657 0.513585 -0.128979
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose,
data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.3384 -0.6634 0.0112 0.6148 3.0952
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.231609 0.669488 0.346 0.72945
currentDF[, TRAIT] 0.063184 0.032805 1.926 0.05439 .
Age -0.008096 0.003958 -2.046 0.04107 *
Gendermale -0.011671 0.070307 -0.166 0.86819
Hypertension.compositeyes 0.055446 0.096058 0.577 0.56393
DiabetesStatusDiabetes -0.086441 0.076316 -1.133 0.25763
SmokerCurrentyes 0.041023 0.070053 0.586 0.55828
Med.Statin.LLDyes -0.358662 0.077004 -4.658 3.64e-06 ***
Med.all.antiplateletyes 0.027783 0.104869 0.265 0.79111
GFR_MDRD -0.003029 0.001699 -1.782 0.07499 .
BMI -0.005242 0.008888 -0.590 0.55546
CAD_history -0.074743 0.071673 -1.043 0.29728
Stroke_history 0.082865 0.067031 1.236 0.21668
Peripheral.interv 0.221438 0.083614 2.648 0.00822 **
stenose50-70% 0.529972 0.508326 1.043 0.29740
stenose70-90% 0.840789 0.494103 1.702 0.08914 .
stenose90-99% 0.964938 0.494012 1.953 0.05107 .
stenose100% (Occlusion) 0.633483 0.603460 1.050 0.29409
stenose50-99% 0.515776 0.693804 0.743 0.45742
stenose70-99% -0.098253 0.634854 -0.155 0.87704
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9785 on 975 degrees of freedom
Multiple R-squared: 0.06621, Adjusted R-squared: 0.04801
F-statistic: 3.638 on 19 and 975 DF, p-value: 2.505e-07
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_rank ' with ' SMC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6R_pg_ug_2015_rank
Trait/outcome.............: SMC_rank
Effect size...............: 0.063184
Standard error............: 0.032805
Odds ratio (effect size)..: 1.065
Lower 95% CI..............: 0.999
Upper 95% CI..............: 1.136
T-value...................: 1.926004
P-value...................: 0.05439434
R^2.......................: 0.066208
Adjusted r^2..............: 0.048011
Sample size of AE DB......: 2388
Sample size of model......: 995
Missing data %............: 58.33333
- processing VesselDensity_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
DiabetesStatus + Med.Statin.LLD + GFR_MDRD + CAD_history +
Peripheral.interv + stenose, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age DiabetesStatusDiabetes Med.Statin.LLDyes
0.320815 0.075290 -0.010262 -0.132040 -0.344402
GFR_MDRD CAD_history Peripheral.interv stenose50-70% stenose70-90%
-0.003693 -0.111030 0.215654 0.616697 0.932206
stenose90-99% stenose100% (Occlusion) stenose50-99% stenose70-99%
1.048426 0.702021 0.580092 0.024660
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose,
data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.3853 -0.6424 -0.0008 0.5946 3.0704
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.373101 0.681739 0.547 0.58432
currentDF[, TRAIT] 0.072980 0.033447 2.182 0.02937 *
Age -0.010508 0.004055 -2.591 0.00971 **
Gendermale -0.045887 0.071954 -0.638 0.52381
Hypertension.compositeyes 0.058845 0.099784 0.590 0.55552
DiabetesStatusDiabetes -0.127065 0.080829 -1.572 0.11629
SmokerCurrentyes 0.028334 0.072970 0.388 0.69789
Med.Statin.LLDyes -0.349043 0.079153 -4.410 1.16e-05 ***
Med.all.antiplateletyes -0.007124 0.110152 -0.065 0.94844
GFR_MDRD -0.003437 0.001780 -1.931 0.05382 .
BMI -0.003941 0.009220 -0.427 0.66914
CAD_history -0.096726 0.074536 -1.298 0.19472
Stroke_history 0.080324 0.069644 1.153 0.24907
Peripheral.interv 0.214445 0.087779 2.443 0.01475 *
stenose50-70% 0.614862 0.512332 1.200 0.23040
stenose70-90% 0.932201 0.496101 1.879 0.06056 .
stenose90-99% 1.047922 0.495616 2.114 0.03475 *
stenose100% (Occlusion) 0.712439 0.605740 1.176 0.23984
stenose50-99% 0.549565 0.695939 0.790 0.42992
stenose70-99% 0.027142 0.697472 0.039 0.96897
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9817 on 912 degrees of freedom
Multiple R-squared: 0.06911, Adjusted R-squared: 0.04972
F-statistic: 3.564 on 19 and 912 DF, p-value: 4.323e-07
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_rank ' with ' VesselDensity_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6R_pg_ug_2015_rank
Trait/outcome.............: VesselDensity_rank
Effect size...............: 0.07298
Standard error............: 0.033447
Odds ratio (effect size)..: 1.076
Lower 95% CI..............: 1.007
Upper 95% CI..............: 1.149
T-value...................: 2.181987
P-value...................: 0.02936508
R^2.......................: 0.069115
Adjusted r^2..............: 0.049721
Sample size of AE DB......: 2388
Sample size of model......: 932
Missing data %............: 60.97152
Analysis of MCP1_pg_ug_2015_rank.
- processing Macrophages_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Hypertension.composite +
Med.Statin.LLD + Stroke_history, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Hypertension.compositeyes Med.Statin.LLDyes Stroke_history
0.26759 -0.05375 -0.20588 -0.15187 0.10863
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose,
data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.3648 -0.6660 -0.0273 0.6576 3.2393
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.2124414 0.6391679 0.332 0.7397
currentDF[, TRAIT] -0.0594184 0.0313284 -1.897 0.0582 .
Age -0.0020052 0.0038961 -0.515 0.6069
Gendermale 0.0857202 0.0691441 1.240 0.2154
Hypertension.compositeyes -0.1946501 0.0966828 -2.013 0.0443 *
DiabetesStatusDiabetes -0.0356636 0.0763648 -0.467 0.6406
SmokerCurrentyes -0.0229331 0.0699413 -0.328 0.7431
Med.Statin.LLDyes -0.1530759 0.0772298 -1.982 0.0477 *
Med.all.antiplateletyes -0.0060653 0.1048021 -0.058 0.9539
GFR_MDRD -0.0007891 0.0016718 -0.472 0.6370
BMI -0.0033761 0.0086790 -0.389 0.6974
CAD_history -0.0429034 0.0715219 -0.600 0.5487
Stroke_history 0.1049280 0.0673948 1.557 0.1198
Peripheral.interv 0.0201952 0.0827808 0.244 0.8073
stenose50-70% 0.2746486 0.4665000 0.589 0.5562
stenose70-90% 0.3718106 0.4510306 0.824 0.4099
stenose90-99% 0.2388238 0.4508856 0.530 0.5965
stenose100% (Occlusion) -0.1876128 0.5732084 -0.327 0.7435
stenose50-99% 0.5643377 0.6719949 0.840 0.4012
stenose70-99% 0.5005227 0.6075952 0.824 0.4103
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9995 on 1017 degrees of freedom
Multiple R-squared: 0.02434, Adjusted R-squared: 0.006109
F-statistic: 1.335 on 19 and 1017 DF, p-value: 0.152
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' Macrophages_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: Macrophages_rank
Effect size...............: -0.059418
Standard error............: 0.031328
Odds ratio (effect size)..: 0.942
Lower 95% CI..............: 0.886
Upper 95% CI..............: 1.002
T-value...................: -1.896633
P-value...................: 0.05815957
R^2.......................: 0.024337
Adjusted r^2..............: 0.006109
Sample size of AE DB......: 2388
Sample size of model......: 1037
Missing data %............: 56.57454
- processing SMC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Hypertension.composite +
Med.Statin.LLD + Stroke_history, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Hypertension.compositeyes Med.Statin.LLDyes Stroke_history
0.27925 -0.10750 -0.20781 -0.15930 0.09907
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose,
data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.3724 -0.6767 -0.0304 0.6444 3.2770
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.290952 0.638800 0.455 0.64887
currentDF[, TRAIT] -0.107077 0.032598 -3.285 0.00106 **
Age -0.003437 0.003929 -0.875 0.38199
Gendermale 0.040662 0.069945 0.581 0.56115
Hypertension.compositeyes -0.190633 0.096558 -1.974 0.04862 *
DiabetesStatusDiabetes -0.029269 0.076250 -0.384 0.70116
SmokerCurrentyes -0.017656 0.069895 -0.253 0.80062
Med.Statin.LLDyes -0.166237 0.077217 -2.153 0.03157 *
Med.all.antiplateletyes -0.011125 0.104641 -0.106 0.91535
GFR_MDRD -0.000614 0.001672 -0.367 0.71354
BMI -0.003254 0.008677 -0.375 0.70773
CAD_history -0.036723 0.071522 -0.513 0.60775
Stroke_history 0.097831 0.067275 1.454 0.14620
Peripheral.interv 0.024988 0.083042 0.301 0.76354
stenose50-70% 0.320528 0.465811 0.688 0.49154
stenose70-90% 0.410560 0.450412 0.912 0.36224
stenose90-99% 0.287171 0.450366 0.638 0.52385
stenose100% (Occlusion) -0.118251 0.572338 -0.207 0.83636
stenose50-99% 0.632607 0.670986 0.943 0.34601
stenose70-99% 0.549146 0.606689 0.905 0.36560
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9976 on 1013 degrees of freedom
Multiple R-squared: 0.031, Adjusted R-squared: 0.01282
F-statistic: 1.705 on 19 and 1013 DF, p-value: 0.02996
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' SMC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: SMC_rank
Effect size...............: -0.107077
Standard error............: 0.032598
Odds ratio (effect size)..: 0.898
Lower 95% CI..............: 0.843
Upper 95% CI..............: 0.958
T-value...................: -3.284785
P-value...................: 0.001055572
R^2.......................: 0.030996
Adjusted r^2..............: 0.012821
Sample size of AE DB......: 2388
Sample size of model......: 1033
Missing data %............: 56.74204
- processing VesselDensity_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Hypertension.composite +
Med.Statin.LLD + Stroke_history, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Hypertension.compositeyes Med.Statin.LLDyes Stroke_history
0.2520 -0.1458 -0.1882 -0.1728 0.1253
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose,
data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.2433 -0.6661 -0.0246 0.6386 3.3145
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.211623 0.650353 0.325 0.7450
currentDF[, TRAIT] -0.142888 0.033041 -4.325 1.69e-05 ***
Age -0.001066 0.004023 -0.265 0.7910
Gendermale 0.102130 0.071463 1.429 0.1533
Hypertension.compositeyes -0.184342 0.100225 -1.839 0.0662 .
DiabetesStatusDiabetes -0.041305 0.080612 -0.512 0.6085
SmokerCurrentyes -0.012813 0.072721 -0.176 0.8602
Med.Statin.LLDyes -0.172532 0.079341 -2.175 0.0299 *
Med.all.antiplateletyes 0.032519 0.109658 0.297 0.7669
GFR_MDRD -0.001284 0.001744 -0.736 0.4619
BMI -0.002257 0.008979 -0.251 0.8016
CAD_history -0.038172 0.074308 -0.514 0.6076
Stroke_history 0.119804 0.069831 1.716 0.0866 .
Peripheral.interv 0.002460 0.087239 0.028 0.9775
stenose50-70% 0.112862 0.469976 0.240 0.8103
stenose70-90% 0.252067 0.452625 0.557 0.5777
stenose90-99% 0.135540 0.452129 0.300 0.7644
stenose100% (Occlusion) -0.268393 0.574820 -0.467 0.6407
stenose50-99% 0.600081 0.673103 0.892 0.3729
stenose70-99% 0.162929 0.674146 0.242 0.8091
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.001 on 949 degrees of freedom
Multiple R-squared: 0.0426, Adjusted R-squared: 0.02343
F-statistic: 2.222 on 19 and 949 DF, p-value: 0.001956
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' VesselDensity_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: VesselDensity_rank
Effect size...............: -0.142888
Standard error............: 0.033041
Odds ratio (effect size)..: 0.867
Lower 95% CI..............: 0.812
Upper 95% CI..............: 0.925
T-value...................: -4.324596
P-value...................: 1.689717e-05
R^2.......................: 0.0426
Adjusted r^2..............: 0.023432
Sample size of AE DB......: 2388
Sample size of model......: 969
Missing data %............: 59.42211 cat("Edit the column names...\n")Edit the column names...colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
"Beta", "s.e.m.",
"OR", "low95CI", "up95CI",
"T-value", "P-value", "r^2", "r^2_adj", "AE_N", "Model_N", "Perc_Miss")
cat("Correct the variable types...\n")Correct the variable types...GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)
# Save the data
cat("Writing results to Excel-file...\n")Writing results to Excel-file...### Univariate
library(openxlsx)
write.xlsx(GLM.results,
file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Con.Multi.Protein.PlaquePhenotypes.RANK.MODEL2.xlsx"),
row.names = FALSE, col.names = TRUE, sheetName = "Con.Multi.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")Removing intermediate files...rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)
Analysis of binary plaque traits as a function of serum/plaque IL6(R)/MCP1 levels.
GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
PROTEIN = TRAITS.PROTEIN.RANK[protein]
cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
for (trait in 1:length(TRAITS.BIN)) {
TRAIT = TRAITS.BIN[trait]
cat(paste0("\n- processing ",TRAIT,"\n\n"))
currentDF <- as.data.frame(AEDB.CEA %>%
dplyr::select(., PROTEIN, TRAIT, COVARIATES_M2) %>%
filter(complete.cases(.))) %>%
filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
# for debug
# print(DT::datatable(currentDF))
# print(nrow(currentDF))
# print(str(currentDF))
# print(class(currentDF[,TRAIT]))
### univariate
fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender +
Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose,
data = currentDF, family = binomial(link = "logit"))
model_step <- stepAIC(fit, direction = "both", trace = FALSE)
print(model_step)
print(summary(fit))
GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
GLM.results = rbind(GLM.results, GLM.results.TEMP)
}
}
Analysis of IL6_rank.
- processing CalcificationPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
GFR_MDRD + Stroke_history, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] GFR_MDRD Stroke_history
1.13102 0.16676 -0.01009 -0.39756
Degrees of Freedom: 480 Total (i.e. Null); 477 Residual
Null Deviance: 656.9
Residual Deviance: 646.2 AIC: 654.2
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.8778 -1.2477 0.8434 1.0452 1.6363
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -1.142674 1.894433 -0.603 0.5464
currentDF[, PROTEIN] 0.187641 0.099762 1.881 0.0600 .
Age 0.005528 0.012359 0.447 0.6547
Gendermale -0.115162 0.218320 -0.527 0.5979
Hypertension.compositeyes 0.476111 0.285756 1.666 0.0957 .
DiabetesStatusDiabetes -0.321528 0.244112 -1.317 0.1878
SmokerCurrentyes 0.107466 0.208795 0.515 0.6068
Med.Statin.LLDyes 0.116419 0.219777 0.530 0.5963
Med.all.antiplateletyes 0.394578 0.343182 1.150 0.2502
GFR_MDRD -0.010480 0.005580 -1.878 0.0603 .
BMI -0.004079 0.026551 -0.154 0.8779
CAD_history -0.050297 0.216490 -0.232 0.8163
Stroke_history -0.449958 0.205657 -2.188 0.0287 *
Peripheral.interv -0.283628 0.243087 -1.167 0.2433
stenose50-70% 1.612811 1.359371 1.186 0.2354
stenose70-90% 1.574761 1.264171 1.246 0.2129
stenose90-99% 1.246104 1.258956 0.990 0.3223
stenose100% (Occlusion) 1.636060 1.495055 1.094 0.2738
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 656.88 on 480 degrees of freedom
Residual deviance: 635.08 on 463 degrees of freedom
AIC: 671.08
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_rank ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_rank
Trait/outcome.............: CalcificationPlaque
Effect size...............: 0.187641
Standard error............: 0.099762
Odds ratio (effect size)..: 1.206
Lower 95% CI..............: 0.992
Upper 95% CI..............: 1.467
Z-value...................: 1.880896
P-value...................: 0.05998602
Hosmer and Lemeshow r^2...: 0.033177
Cox and Snell r^2.........: 0.044296
Nagelkerke's pseudo r^2...: 0.059475
Sample size of AE DB......: 2388
Sample size of model......: 481
Missing data %............: 79.85762
- processing CollagenPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ SmokerCurrent +
Peripheral.interv + stenose, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) SmokerCurrentyes Peripheral.interv stenose50-70% stenose70-90%
16.6172 0.5118 -0.5187 -0.1656 -15.0805
stenose90-99% stenose100% (Occlusion)
-15.4792 -14.7976
Degrees of Freedom: 479 Total (i.e. Null); 473 Residual
Null Deviance: 474.8
Residual Deviance: 455.6 AIC: 469.6
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.1410 0.4147 0.5880 0.7112 1.1491
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.833e+01 2.202e+03 0.008 0.9934
currentDF[, PROTEIN] -1.131e-01 1.218e-01 -0.928 0.3532
Age -5.156e-03 1.583e-02 -0.326 0.7447
Gendermale -1.905e-01 2.800e-01 -0.680 0.4962
Hypertension.compositeyes 3.476e-01 3.454e-01 1.006 0.3142
DiabetesStatusDiabetes 1.851e-01 3.131e-01 0.591 0.5545
SmokerCurrentyes 5.231e-01 2.725e-01 1.920 0.0549 .
Med.Statin.LLDyes -1.921e-02 2.739e-01 -0.070 0.9441
Med.all.antiplateletyes 5.197e-01 3.990e-01 1.303 0.1927
GFR_MDRD -4.836e-03 7.078e-03 -0.683 0.4945
BMI -2.662e-02 3.364e-02 -0.791 0.4287
CAD_history 9.202e-02 2.697e-01 0.341 0.7330
Stroke_history 1.321e-01 2.634e-01 0.502 0.6159
Peripheral.interv -5.602e-01 2.846e-01 -1.969 0.0490 *
stenose50-70% -1.090e-01 2.395e+03 0.000 1.0000
stenose70-90% -1.607e+01 2.202e+03 -0.007 0.9942
stenose90-99% -1.653e+01 2.202e+03 -0.008 0.9940
stenose100% (Occlusion) -1.556e+01 2.202e+03 -0.007 0.9944
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 474.79 on 479 degrees of freedom
Residual deviance: 449.41 on 462 degrees of freedom
AIC: 485.41
Number of Fisher Scoring iterations: 16
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_rank ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_rank
Trait/outcome.............: CollagenPlaque
Effect size...............: -0.113087
Standard error............: 0.121798
Odds ratio (effect size)..: 0.893
Lower 95% CI..............: 0.703
Upper 95% CI..............: 1.134
Z-value...................: -0.928484
P-value...................: 0.3531568
Hosmer and Lemeshow r^2...: 0.053459
Cox and Snell r^2.........: 0.051505
Nagelkerke's pseudo r^2...: 0.082
Sample size of AE DB......: 2388
Sample size of model......: 480
Missing data %............: 79.8995
- processing Fat10Perc
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Gender + Hypertension.composite +
DiabetesStatus + Stroke_history, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) Gendermale Hypertension.compositeyes DiabetesStatusDiabetes Stroke_history
0.2536 0.7695 0.6023 -0.5270 0.7668
Degrees of Freedom: 480 Total (i.e. Null); 476 Residual
Null Deviance: 483.6
Residual Deviance: 459.4 AIC: 469.4
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.4561 0.3832 0.5522 0.7048 1.3298
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 13.557573 833.530464 0.016 0.987023
currentDF[, PROTEIN] 0.105467 0.121292 0.870 0.384555
Age -0.008356 0.015529 -0.538 0.590504
Gendermale 0.854341 0.255518 3.344 0.000827 ***
Hypertension.compositeyes 0.653898 0.331496 1.973 0.048545 *
DiabetesStatusDiabetes -0.535682 0.288011 -1.860 0.062894 .
SmokerCurrentyes 0.235127 0.266099 0.884 0.376909
Med.Statin.LLDyes -0.270417 0.289334 -0.935 0.349985
Med.all.antiplateletyes 0.332141 0.405082 0.820 0.412253
GFR_MDRD -0.004346 0.007159 -0.607 0.543790
BMI 0.025693 0.033034 0.778 0.436702
CAD_history -0.100692 0.271063 -0.371 0.710285
Stroke_history 0.725302 0.285075 2.544 0.010951 *
Peripheral.interv -0.253404 0.288452 -0.878 0.379674
stenose50-70% -14.083308 833.528719 -0.017 0.986520
stenose70-90% -13.205044 833.528557 -0.016 0.987360
stenose90-99% -13.322823 833.528546 -0.016 0.987247
stenose100% (Occlusion) -12.920301 833.529308 -0.016 0.987633
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 483.60 on 480 degrees of freedom
Residual deviance: 450.33 on 463 degrees of freedom
AIC: 486.33
Number of Fisher Scoring iterations: 14
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_rank ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_rank
Trait/outcome.............: Fat10Perc
Effect size...............: 0.105467
Standard error............: 0.121292
Odds ratio (effect size)..: 1.111
Lower 95% CI..............: 0.876
Upper 95% CI..............: 1.409
Z-value...................: 0.869535
P-value...................: 0.3845546
Hosmer and Lemeshow r^2...: 0.068787
Cox and Snell r^2.........: 0.066821
Nagelkerke's pseudo r^2...: 0.105379
Sample size of AE DB......: 2388
Sample size of model......: 481
Missing data %............: 79.85762
- processing IPH
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + Gender +
DiabetesStatus + BMI + Peripheral.interv, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) Age Gendermale DiabetesStatusDiabetes BMI
-2.71906 0.03306 0.64772 -0.58799 0.04663
Peripheral.interv
0.47594
Degrees of Freedom: 480 Total (i.e. Null); 475 Residual
Null Deviance: 538.2
Residual Deviance: 514.6 AIC: 526.6
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.1197 0.3817 0.6180 0.7868 1.3147
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -3.724347 2.077712 -1.793 0.07305 .
currentDF[, PROTEIN] 0.031883 0.112786 0.283 0.77742
Age 0.029801 0.014081 2.116 0.03431 *
Gendermale 0.677626 0.239517 2.829 0.00467 **
Hypertension.compositeyes 0.362423 0.313321 1.157 0.24739
DiabetesStatusDiabetes -0.638623 0.268045 -2.383 0.01719 *
SmokerCurrentyes 0.133395 0.241854 0.552 0.58125
Med.Statin.LLDyes 0.051499 0.255894 0.201 0.84050
Med.all.antiplateletyes -0.278418 0.430917 -0.646 0.51821
GFR_MDRD -0.004065 0.006460 -0.629 0.52923
BMI 0.046769 0.030726 1.522 0.12797
CAD_history 0.176307 0.258493 0.682 0.49520
Stroke_history 0.049418 0.239972 0.206 0.83685
Peripheral.interv 0.442713 0.300800 1.472 0.14108
stenose50-70% 1.305170 1.391394 0.938 0.34823
stenose70-90% 1.209027 1.271245 0.951 0.34158
stenose90-99% 1.359738 1.267498 1.073 0.28337
stenose100% (Occlusion) 1.878451 1.699793 1.105 0.26911
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 538.20 on 480 degrees of freedom
Residual deviance: 509.11 on 463 degrees of freedom
AIC: 545.11
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_rank ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_rank
Trait/outcome.............: IPH
Effect size...............: 0.031883
Standard error............: 0.112786
Odds ratio (effect size)..: 1.032
Lower 95% CI..............: 0.828
Upper 95% CI..............: 1.288
Z-value...................: 0.282686
P-value...................: 0.7774176
Hosmer and Lemeshow r^2...: 0.054057
Cox and Snell r^2.........: 0.058693
Nagelkerke's pseudo r^2...: 0.087163
Sample size of AE DB......: 2388
Sample size of model......: 481
Missing data %............: 79.85762
Analysis of MCP1_rank.
- processing CalcificationPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
DiabetesStatus + GFR_MDRD + Stroke_history, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] DiabetesStatusDiabetes GFR_MDRD Stroke_history
1.192770 -0.151103 -0.450022 -0.009032 -0.335659
Degrees of Freedom: 513 Total (i.e. Null); 509 Residual
Null Deviance: 698.1
Residual Deviance: 685.4 AIC: 695.4
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.6902 -1.2563 0.8474 1.0134 1.4941
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -1.341239 1.847433 -0.726 0.4678
currentDF[, PROTEIN] -0.143925 0.094902 -1.517 0.1294
Age 0.008539 0.012074 0.707 0.4794
Gendermale -0.133609 0.211820 -0.631 0.5282
Hypertension.compositeyes 0.421229 0.275933 1.527 0.1269
DiabetesStatusDiabetes -0.491397 0.233262 -2.107 0.0351 *
SmokerCurrentyes 0.190422 0.202282 0.941 0.3465
Med.Statin.LLDyes -0.065611 0.216144 -0.304 0.7615
Med.all.antiplateletyes 0.294148 0.328102 0.897 0.3700
GFR_MDRD -0.008637 0.005278 -1.636 0.1018
BMI 0.006020 0.025217 0.239 0.8113
CAD_history -0.019955 0.210077 -0.095 0.9243
Stroke_history -0.382267 0.196922 -1.941 0.0522 .
Peripheral.interv -0.296732 0.245678 -1.208 0.2271
stenose50-70% 1.345003 1.366009 0.985 0.3248
stenose70-90% 1.474735 1.278492 1.153 0.2487
stenose90-99% 1.183503 1.274224 0.929 0.3530
stenose100% (Occlusion) 1.433390 1.603296 0.894 0.3713
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 698.10 on 513 degrees of freedom
Residual deviance: 676.62 on 496 degrees of freedom
AIC: 712.62
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: CalcificationPlaque
Effect size...............: -0.143925
Standard error............: 0.094902
Odds ratio (effect size)..: 0.866
Lower 95% CI..............: 0.719
Upper 95% CI..............: 1.043
Z-value...................: -1.516566
P-value...................: 0.1293764
Hosmer and Lemeshow r^2...: 0.030769
Cox and Snell r^2.........: 0.040928
Nagelkerke's pseudo r^2...: 0.055094
Sample size of AE DB......: 2388
Sample size of model......: 514
Missing data %............: 78.47571
- processing CollagenPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
SmokerCurrent + Med.all.antiplatelet + Peripheral.interv +
stenose, family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] SmokerCurrentyes Med.all.antiplateletyes Peripheral.interv
15.0605 -0.6325 0.5715 0.8102 -0.5723
stenose50-70% stenose70-90% stenose90-99% stenose100% (Occlusion)
-13.0595 -14.0415 -14.5745 -14.0760
Degrees of Freedom: 511 Total (i.e. Null); 503 Residual
Null Deviance: 505.7
Residual Deviance: 459.5 AIC: 477.5
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.5465 0.3165 0.5104 0.6799 1.4542
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 15.999553 778.354658 0.021 0.9836
currentDF[, PROTEIN] -0.622928 0.128740 -4.839 1.31e-06 ***
Age -0.005270 0.015877 -0.332 0.7399
Gendermale -0.109271 0.281106 -0.389 0.6975
Hypertension.compositeyes 0.232635 0.346594 0.671 0.5021
DiabetesStatusDiabetes 0.322371 0.320516 1.006 0.3145
SmokerCurrentyes 0.569775 0.272057 2.094 0.0362 *
Med.Statin.LLDyes 0.069345 0.268917 0.258 0.7965
Med.all.antiplateletyes 0.802899 0.388040 2.069 0.0385 *
GFR_MDRD -0.002382 0.006955 -0.343 0.7320
BMI -0.025431 0.033840 -0.752 0.4523
CAD_history 0.128771 0.267709 0.481 0.6305
Stroke_history 0.133460 0.258321 0.517 0.6054
Peripheral.interv -0.629923 0.299489 -2.103 0.0354 *
stenose50-70% -13.094548 778.353270 -0.017 0.9866
stenose70-90% -14.077384 778.352576 -0.018 0.9856
stenose90-99% -14.620319 778.352564 -0.019 0.9850
stenose100% (Occlusion) -14.008097 778.353486 -0.018 0.9856
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 505.69 on 511 degrees of freedom
Residual deviance: 456.43 on 494 degrees of freedom
AIC: 492.43
Number of Fisher Scoring iterations: 14
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: CollagenPlaque
Effect size...............: -0.622928
Standard error............: 0.12874
Odds ratio (effect size)..: 0.536
Lower 95% CI..............: 0.417
Upper 95% CI..............: 0.69
Z-value...................: -4.83865
P-value...................: 1.307237e-06
Hosmer and Lemeshow r^2...: 0.097413
Cox and Snell r^2.........: 0.091729
Nagelkerke's pseudo r^2...: 0.146168
Sample size of AE DB......: 2388
Sample size of model......: 512
Missing data %............: 78.55946
- processing Fat10Perc
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Gender + Hypertension.composite + Stroke_history, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Gendermale Hypertension.compositeyes Stroke_history
0.4409 0.6533 0.5583 0.6017 0.6751
Degrees of Freedom: 513 Total (i.e. Null); 509 Residual
Null Deviance: 506.6
Residual Deviance: 459.5 AIC: 469.5
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.5740 0.2922 0.4930 0.6720 1.5683
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 12.661734 817.641489 0.015 0.9876
currentDF[, PROTEIN] 0.656649 0.132397 4.960 7.06e-07 ***
Age 0.001732 0.015546 0.111 0.9113
Gendermale 0.612693 0.256582 2.388 0.0169 *
Hypertension.compositeyes 0.685985 0.341187 2.011 0.0444 *
DiabetesStatusDiabetes -0.262261 0.294365 -0.891 0.3730
SmokerCurrentyes 0.363236 0.268874 1.351 0.1767
Med.Statin.LLDyes -0.147175 0.293177 -0.502 0.6157
Med.all.antiplateletyes 0.038168 0.411206 0.093 0.9260
GFR_MDRD -0.001070 0.007032 -0.152 0.8790
BMI 0.041989 0.032506 1.292 0.1965
CAD_history -0.184485 0.275017 -0.671 0.5023
Stroke_history 0.635540 0.279528 2.274 0.0230 *
Peripheral.interv -0.174872 0.301205 -0.581 0.5615
stenose50-70% -14.255551 817.639782 -0.017 0.9861
stenose70-90% -13.148729 817.639612 -0.016 0.9872
stenose90-99% -13.458669 817.639598 -0.016 0.9869
stenose100% (Occlusion) -13.167887 817.640432 -0.016 0.9872
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 506.55 on 513 degrees of freedom
Residual deviance: 448.02 on 496 degrees of freedom
AIC: 484.02
Number of Fisher Scoring iterations: 14
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: Fat10Perc
Effect size...............: 0.656649
Standard error............: 0.132397
Odds ratio (effect size)..: 1.928
Lower 95% CI..............: 1.488
Upper 95% CI..............: 2.5
Z-value...................: 4.959683
P-value...................: 7.060839e-07
Hosmer and Lemeshow r^2...: 0.115562
Cox and Snell r^2.........: 0.107642
Nagelkerke's pseudo r^2...: 0.171745
Sample size of AE DB......: 2388
Sample size of model......: 514
Missing data %............: 78.47571
- processing IPH
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + Gender +
DiabetesStatus + BMI + Peripheral.interv, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) Age Gendermale DiabetesStatusDiabetes BMI
-2.41015 0.02549 0.72976 -0.55880 0.05326
Peripheral.interv
0.46653
Degrees of Freedom: 513 Total (i.e. Null); 508 Residual
Null Deviance: 568
Residual Deviance: 544 AIC: 556
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.1812 0.4279 0.6129 0.7610 1.5453
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -3.337489 2.007482 -1.663 0.09641 .
currentDF[, PROTEIN] 0.114993 0.110404 1.042 0.29761
Age 0.021692 0.013848 1.566 0.11724
Gendermale 0.699221 0.231008 3.027 0.00247 **
Hypertension.compositeyes 0.332762 0.307165 1.083 0.27866
DiabetesStatusDiabetes -0.570391 0.259801 -2.195 0.02813 *
SmokerCurrentyes 0.111871 0.236363 0.473 0.63600
Med.Statin.LLDyes -0.055247 0.255070 -0.217 0.82852
Med.all.antiplateletyes -0.189324 0.397135 -0.477 0.63356
GFR_MDRD -0.003295 0.006174 -0.534 0.59352
BMI 0.051262 0.029169 1.757 0.07884 .
CAD_history 0.218815 0.255681 0.856 0.39210
Stroke_history 0.130905 0.232489 0.563 0.57339
Peripheral.interv 0.448115 0.306829 1.460 0.14416
stenose50-70% 1.269161 1.380367 0.919 0.35787
stenose70-90% 1.185950 1.267384 0.936 0.34940
stenose90-99% 1.355527 1.264520 1.072 0.28373
stenose100% (Occlusion) 1.465751 1.720671 0.852 0.39430
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 567.98 on 513 degrees of freedom
Residual deviance: 538.07 on 496 degrees of freedom
AIC: 574.07
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: IPH
Effect size...............: 0.114993
Standard error............: 0.110404
Odds ratio (effect size)..: 1.122
Lower 95% CI..............: 0.904
Upper 95% CI..............: 1.393
Z-value...................: 1.041567
P-value...................: 0.2976126
Hosmer and Lemeshow r^2...: 0.052662
Cox and Snell r^2.........: 0.056532
Nagelkerke's pseudo r^2...: 0.084528
Sample size of AE DB......: 2388
Sample size of model......: 514
Missing data %............: 78.47571
Analysis of IL6_pg_ug_2015_rank.
- processing CalcificationPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + SmokerCurrent + CAD_history + stenose, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Age SmokerCurrentyes CAD_history
-1.21906 -0.10139 0.01999 0.39841 0.25581
stenose50-70% stenose70-90% stenose90-99% stenose100% (Occlusion) stenose50-99%
-1.00567 -0.50697 -0.26246 0.80569 -13.93538
stenose70-99%
-1.53003
Degrees of Freedom: 996 Total (i.e. Null); 986 Residual
Null Deviance: 1381
Residual Deviance: 1349 AIC: 1371
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.6147 -1.1333 -0.7964 1.1564 1.6722
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.909944 1.321284 -0.689 0.49102
currentDF[, PROTEIN] -0.099976 0.066163 -1.511 0.13077
Age 0.017028 0.008100 2.102 0.03552 *
Gendermale -0.132850 0.144146 -0.922 0.35672
Hypertension.compositeyes 0.232383 0.202519 1.147 0.25119
DiabetesStatusDiabetes -0.175024 0.159524 -1.097 0.27257
SmokerCurrentyes 0.413549 0.146703 2.819 0.00482 **
Med.Statin.LLDyes -0.164639 0.159153 -1.034 0.30092
Med.all.antiplateletyes -0.223519 0.217193 -1.029 0.30342
GFR_MDRD -0.001873 0.003492 -0.536 0.59169
BMI 0.012482 0.018095 0.690 0.49034
CAD_history 0.264314 0.149945 1.763 0.07795 .
Stroke_history -0.138429 0.140758 -0.983 0.32538
Peripheral.interv -0.183619 0.172919 -1.062 0.28829
stenose50-70% -0.939975 0.962505 -0.977 0.32877
stenose70-90% -0.485844 0.928836 -0.523 0.60093
stenose90-99% -0.236697 0.928355 -0.255 0.79875
stenose100% (Occlusion) 0.801826 1.244197 0.644 0.51928
stenose50-99% -14.007873 368.424975 -0.038 0.96967
stenose70-99% -1.453878 1.253623 -1.160 0.24615
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1381.3 on 996 degrees of freedom
Residual deviance: 1340.3 on 977 degrees of freedom
AIC: 1380.3
Number of Fisher Scoring iterations: 12
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_rank ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_pg_ug_2015_rank
Trait/outcome.............: CalcificationPlaque
Effect size...............: -0.099976
Standard error............: 0.066163
Odds ratio (effect size)..: 0.905
Lower 95% CI..............: 0.795
Upper 95% CI..............: 1.03
Z-value...................: -1.511065
P-value...................: 0.1307718
Hosmer and Lemeshow r^2...: 0.029706
Cox and Snell r^2.........: 0.04032
Nagelkerke's pseudo r^2...: 0.053776
Sample size of AE DB......: 2388
Sample size of model......: 997
Missing data %............: 58.24958
- processing CollagenPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Hypertension.composite + SmokerCurrent + BMI + Stroke_history,
family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Hypertension.compositeyes SmokerCurrentyes BMI
-0.01981 -0.29027 0.34009 0.44475 0.03349
Stroke_history
0.25226
Degrees of Freedom: 999 Total (i.e. Null); 994 Residual
Null Deviance: 1017
Residual Deviance: 993 AIC: 1005
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.2838 0.4520 0.6083 0.7205 1.0945
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.292e+01 6.481e+02 0.020 0.984089
currentDF[, PROTEIN] -2.788e-01 8.257e-02 -3.376 0.000735 ***
Age 8.333e-03 9.876e-03 0.844 0.398752
Gendermale -8.397e-02 1.781e-01 -0.471 0.637300
Hypertension.compositeyes 3.107e-01 2.330e-01 1.334 0.182365
DiabetesStatusDiabetes 2.105e-01 2.039e-01 1.033 0.301764
SmokerCurrentyes 4.917e-01 1.863e-01 2.639 0.008305 **
Med.Statin.LLDyes -1.661e-02 1.955e-01 -0.085 0.932280
Med.all.antiplateletyes 1.316e-01 2.617e-01 0.503 0.615014
GFR_MDRD 4.714e-03 4.302e-03 1.096 0.273176
BMI 3.356e-02 2.363e-02 1.420 0.155532
CAD_history 2.203e-01 1.882e-01 1.171 0.241679
Stroke_history 2.405e-01 1.765e-01 1.363 0.172966
Peripheral.interv -1.820e-02 2.155e-01 -0.084 0.932671
stenose50-70% -1.361e+01 6.481e+02 -0.021 0.983249
stenose70-90% -1.401e+01 6.481e+02 -0.022 0.982747
stenose90-99% -1.406e+01 6.481e+02 -0.022 0.982690
stenose100% (Occlusion) 4.945e-01 8.183e+02 0.001 0.999518
stenose50-99% 1.313e-02 1.208e+03 0.000 0.999991
stenose70-99% -1.373e+01 6.481e+02 -0.021 0.983103
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1017.22 on 999 degrees of freedom
Residual deviance: 980.18 on 980 degrees of freedom
AIC: 1020.2
Number of Fisher Scoring iterations: 14
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_rank ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_pg_ug_2015_rank
Trait/outcome.............: CollagenPlaque
Effect size...............: -0.278762
Standard error............: 0.08257
Odds ratio (effect size)..: 0.757
Lower 95% CI..............: 0.644
Upper 95% CI..............: 0.89
Z-value...................: -3.376059
P-value...................: 0.0007353204
Hosmer and Lemeshow r^2...: 0.036411
Cox and Snell r^2.........: 0.036361
Nagelkerke's pseudo r^2...: 0.056956
Sample size of AE DB......: 2388
Sample size of model......: 1000
Missing data %............: 58.12395
- processing Fat10Perc
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Gender + Stroke_history + Peripheral.interv, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Gendermale Stroke_history Peripheral.interv
0.5014 0.4505 0.8327 0.3722 -0.6087
Degrees of Freedom: 999 Total (i.e. Null); 995 Residual
Null Deviance: 1165
Residual Deviance: 1079 AIC: 1089
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.2290 -1.0455 0.6047 0.8064 2.0491
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.317e+01 3.905e+02 0.034 0.973093
currentDF[, PROTEIN] 4.532e-01 7.890e-02 5.744 9.26e-09 ***
Age 9.621e-03 9.328e-03 1.031 0.302330
Gendermale 8.512e-01 1.611e-01 5.282 1.27e-07 ***
Hypertension.compositeyes 6.829e-02 2.349e-01 0.291 0.771250
DiabetesStatusDiabetes -1.155e-01 1.839e-01 -0.628 0.529991
SmokerCurrentyes 1.014e-01 1.710e-01 0.593 0.553366
Med.Statin.LLDyes -1.768e-01 1.904e-01 -0.928 0.353200
Med.all.antiplateletyes 6.371e-02 2.501e-01 0.255 0.798916
GFR_MDRD -2.636e-04 4.098e-03 -0.064 0.948718
BMI 3.776e-03 2.044e-02 0.185 0.853460
CAD_history 7.889e-02 1.751e-01 0.451 0.652297
Stroke_history 3.768e-01 1.703e-01 2.213 0.026912 *
Peripheral.interv -6.136e-01 1.860e-01 -3.299 0.000972 ***
stenose50-70% -1.353e+01 3.905e+02 -0.035 0.972366
stenose70-90% -1.348e+01 3.905e+02 -0.035 0.972456
stenose90-99% -1.333e+01 3.905e+02 -0.034 0.972772
stenose100% (Occlusion) -1.427e+01 3.905e+02 -0.037 0.970836
stenose50-99% -1.498e+01 3.905e+02 -0.038 0.969387
stenose70-99% -1.461e+01 3.905e+02 -0.037 0.970150
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1164.5 on 999 degrees of freedom
Residual deviance: 1067.6 on 980 degrees of freedom
AIC: 1107.6
Number of Fisher Scoring iterations: 13
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_rank ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_pg_ug_2015_rank
Trait/outcome.............: Fat10Perc
Effect size...............: 0.453209
Standard error............: 0.078905
Odds ratio (effect size)..: 1.573
Lower 95% CI..............: 1.348
Upper 95% CI..............: 1.837
Z-value...................: 5.743761
P-value...................: 9.259659e-09
Hosmer and Lemeshow r^2...: 0.083261
Cox and Snell r^2.........: 0.092407
Nagelkerke's pseudo r^2...: 0.134327
Sample size of AE DB......: 2388
Sample size of model......: 1000
Missing data %............: 58.12395
- processing IPH
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Gender + Med.Statin.LLD +
BMI + CAD_history + Stroke_history + stenose, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) Gendermale Med.Statin.LLDyes BMI CAD_history
0.16028 0.59271 -0.25963 0.02923 0.30318
Stroke_history stenose50-70% stenose70-90% stenose90-99% stenose100% (Occlusion)
0.25028 -1.09743 -0.97086 -0.65159 -0.80087
stenose50-99% stenose70-99%
-15.27559 0.59126
Degrees of Freedom: 998 Total (i.e. Null); 987 Residual
Null Deviance: 1331
Residual Deviance: 1288 AIC: 1312
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.9367 -1.2695 0.8101 0.9793 1.4492
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.134237 1.491544 0.090 0.9283
currentDF[, PROTEIN] 0.054522 0.068254 0.799 0.4244
Age 0.001619 0.008307 0.195 0.8455
Gendermale 0.638876 0.146508 4.361 1.3e-05 ***
Hypertension.compositeyes -0.108080 0.207201 -0.522 0.6019
DiabetesStatusDiabetes -0.123290 0.163538 -0.754 0.4509
SmokerCurrentyes 0.124934 0.151554 0.824 0.4097
Med.Statin.LLDyes -0.249499 0.167274 -1.492 0.1358
Med.all.antiplateletyes 0.161931 0.221057 0.733 0.4638
GFR_MDRD -0.004974 0.003614 -1.376 0.1687
BMI 0.033028 0.018696 1.767 0.0773 .
CAD_history 0.310960 0.157200 1.978 0.0479 *
Stroke_history 0.230621 0.146500 1.574 0.1154
Peripheral.interv 0.037076 0.178283 0.208 0.8353
stenose50-70% -1.011322 1.165248 -0.868 0.3854
stenose70-90% -0.904406 1.139736 -0.794 0.4275
stenose90-99% -0.590355 1.139754 -0.518 0.6045
stenose100% (Occlusion) -0.735447 1.357428 -0.542 0.5880
stenose50-99% -15.239630 376.951258 -0.040 0.9678
stenose70-99% 0.597045 1.580966 0.378 0.7057
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1331.0 on 998 degrees of freedom
Residual deviance: 1283.4 on 979 degrees of freedom
AIC: 1323.4
Number of Fisher Scoring iterations: 12
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_rank ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_pg_ug_2015_rank
Trait/outcome.............: IPH
Effect size...............: 0.054522
Standard error............: 0.068254
Odds ratio (effect size)..: 1.056
Lower 95% CI..............: 0.924
Upper 95% CI..............: 1.207
Z-value...................: 0.798812
P-value...................: 0.4243993
Hosmer and Lemeshow r^2...: 0.03579
Cox and Snell r^2.........: 0.046565
Nagelkerke's pseudo r^2...: 0.063256
Sample size of AE DB......: 2388
Sample size of model......: 999
Missing data %............: 58.16583
Analysis of IL6R_pg_ug_2015_rank.
- processing CalcificationPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + Hypertension.composite +
DiabetesStatus + SmokerCurrent + CAD_history + stenose, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) Age Hypertension.compositeyes DiabetesStatusDiabetes SmokerCurrentyes
-0.47387 0.01628 0.29007 -0.24352 0.33077
CAD_history stenose50-70% stenose70-90% stenose90-99% stenose100% (Occlusion)
0.22888 -1.57353 -1.17165 -0.93734 0.08563
stenose50-99% stenose70-99%
-15.69452 -2.08325
Degrees of Freedom: 1000 Total (i.e. Null); 989 Residual
Null Deviance: 1387
Residual Deviance: 1354 AIC: 1378
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.5705 -1.1316 -0.8359 1.1617 1.7412
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.212109 1.495726 0.142 0.8872
currentDF[, PROTEIN] -0.002449 0.066213 -0.037 0.9705
Age 0.013336 0.008150 1.636 0.1018
Gendermale -0.162994 0.143965 -1.132 0.2576
Hypertension.compositeyes 0.294333 0.200281 1.470 0.1417
DiabetesStatusDiabetes -0.249693 0.159825 -1.562 0.1182
SmokerCurrentyes 0.336013 0.145847 2.304 0.0212 *
Med.Statin.LLDyes -0.108235 0.161265 -0.671 0.5021
Med.all.antiplateletyes -0.255972 0.219348 -1.167 0.2432
GFR_MDRD -0.002395 0.003551 -0.674 0.5000
BMI 0.004821 0.018465 0.261 0.7940
CAD_history 0.251978 0.149114 1.690 0.0911 .
Stroke_history -0.166730 0.139636 -1.194 0.2325
Peripheral.interv -0.196876 0.172959 -1.138 0.2550
stenose50-70% -1.465448 1.195541 -1.226 0.2203
stenose70-90% -1.113704 1.168398 -0.953 0.3405
stenose90-99% -0.867558 1.168634 -0.742 0.4579
stenose100% (Occlusion) 0.118872 1.429842 0.083 0.9337
stenose50-99% -15.589875 435.856093 -0.036 0.9715
stenose70-99% -1.997577 1.438041 -1.389 0.1648
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1386.7 on 1000 degrees of freedom
Residual deviance: 1347.6 on 981 degrees of freedom
AIC: 1387.6
Number of Fisher Scoring iterations: 13
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_rank ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6R_pg_ug_2015_rank
Trait/outcome.............: CalcificationPlaque
Effect size...............: -0.002449
Standard error............: 0.066213
Odds ratio (effect size)..: 0.998
Lower 95% CI..............: 0.876
Upper 95% CI..............: 1.136
Z-value...................: -0.036981
P-value...................: 0.9704999
Hosmer and Lemeshow r^2...: 0.028198
Cox and Snell r^2.........: 0.03831
Nagelkerke's pseudo r^2...: 0.051096
Sample size of AE DB......: 2388
Sample size of model......: 1001
Missing data %............: 58.08208
- processing CollagenPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ SmokerCurrent +
CAD_history, family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) SmokerCurrentyes CAD_history
1.1251 0.4158 0.3017
Degrees of Freedom: 1003 Total (i.e. Null); 1001 Residual
Null Deviance: 1022
Residual Deviance: 1014 AIC: 1020
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.1265 0.4975 0.6343 0.7205 0.9708
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.282e+01 7.188e+02 0.018 0.98577
currentDF[, PROTEIN] 8.155e-03 8.142e-02 0.100 0.92022
Age 1.014e-02 9.909e-03 1.023 0.30614
Gendermale 4.618e-02 1.747e-01 0.264 0.79159
Hypertension.compositeyes 2.825e-01 2.303e-01 1.227 0.21991
DiabetesStatusDiabetes 2.034e-01 2.026e-01 1.004 0.31533
SmokerCurrentyes 4.830e-01 1.850e-01 2.610 0.00905 **
Med.Statin.LLDyes 3.976e-02 1.953e-01 0.204 0.83865
Med.all.antiplateletyes 2.293e-01 2.583e-01 0.888 0.37472
GFR_MDRD 4.130e-03 4.355e-03 0.948 0.34288
BMI 2.798e-02 2.331e-02 1.200 0.23000
CAD_history 2.663e-01 1.871e-01 1.423 0.15476
Stroke_history 2.061e-01 1.740e-01 1.184 0.23622
Peripheral.interv 7.680e-02 2.159e-01 0.356 0.72202
stenose50-70% -1.365e+01 7.188e+02 -0.019 0.98485
stenose70-90% -1.411e+01 7.188e+02 -0.020 0.98434
stenose90-99% -1.411e+01 7.188e+02 -0.020 0.98434
stenose100% (Occlusion) 3.208e-01 8.796e+02 0.000 0.99971
stenose50-99% -1.451e-01 1.020e+03 0.000 0.99989
stenose70-99% -1.370e+01 7.188e+02 -0.019 0.98479
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1021.76 on 1003 degrees of freedom
Residual deviance: 996.48 on 984 degrees of freedom
AIC: 1036.5
Number of Fisher Scoring iterations: 14
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_rank ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6R_pg_ug_2015_rank
Trait/outcome.............: CollagenPlaque
Effect size...............: 0.008155
Standard error............: 0.081415
Odds ratio (effect size)..: 1.008
Lower 95% CI..............: 0.859
Upper 95% CI..............: 1.183
Z-value...................: 0.100161
P-value...................: 0.9202161
Hosmer and Lemeshow r^2...: 0.024745
Cox and Snell r^2.........: 0.024868
Nagelkerke's pseudo r^2...: 0.038943
Sample size of AE DB......: 2388
Sample size of model......: 1004
Missing data %............: 57.95645
- processing Fat10Perc
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Gender + Stroke_history + Peripheral.interv, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Gendermale Stroke_history Peripheral.interv
0.4550 0.1229 0.7952 0.4442 -0.6211
Degrees of Freedom: 1003 Total (i.e. Null); 999 Residual
Null Deviance: 1171
Residual Deviance: 1122 AIC: 1132
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.0862 -1.1264 0.6590 0.7969 1.8542
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.332e+01 4.411e+02 0.030 0.97591
currentDF[, PROTEIN] 9.800e-02 7.527e-02 1.302 0.19292
Age 1.132e-02 9.265e-03 1.222 0.22178
Gendermale 8.405e-01 1.583e-01 5.308 1.11e-07 ***
Hypertension.compositeyes -1.069e-03 2.299e-01 -0.005 0.99629
DiabetesStatusDiabetes -1.151e-01 1.801e-01 -0.639 0.52259
SmokerCurrentyes 1.497e-01 1.676e-01 0.894 0.37152
Med.Statin.LLDyes -1.464e-01 1.904e-01 -0.769 0.44205
Med.all.antiplateletyes 4.523e-02 2.493e-01 0.181 0.85603
GFR_MDRD 5.281e-06 4.114e-03 0.001 0.99898
BMI -5.235e-03 2.075e-02 -0.252 0.80084
CAD_history -6.362e-02 1.695e-01 -0.375 0.70743
Stroke_history 4.412e-01 1.660e-01 2.658 0.00787 **
Peripheral.interv -5.960e-01 1.843e-01 -3.234 0.00122 **
stenose50-70% -1.358e+01 4.411e+02 -0.031 0.97545
stenose70-90% -1.349e+01 4.411e+02 -0.031 0.97560
stenose90-99% -1.338e+01 4.411e+02 -0.030 0.97581
stenose100% (Occlusion) -1.416e+01 4.411e+02 -0.032 0.97440
stenose50-99% -1.586e+01 4.411e+02 -0.036 0.97132
stenose70-99% -1.470e+01 4.411e+02 -0.033 0.97341
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1171.0 on 1003 degrees of freedom
Residual deviance: 1106.8 on 984 degrees of freedom
AIC: 1146.8
Number of Fisher Scoring iterations: 13
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_rank ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6R_pg_ug_2015_rank
Trait/outcome.............: Fat10Perc
Effect size...............: 0.097997
Standard error............: 0.075268
Odds ratio (effect size)..: 1.103
Lower 95% CI..............: 0.952
Upper 95% CI..............: 1.278
Z-value...................: 1.301985
P-value...................: 0.1929215
Hosmer and Lemeshow r^2...: 0.054819
Cox and Snell r^2.........: 0.061938
Nagelkerke's pseudo r^2...: 0.08996
Sample size of AE DB......: 2388
Sample size of model......: 1004
Missing data %............: 57.95645
- processing IPH
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Gender + GFR_MDRD + CAD_history, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Gendermale GFR_MDRD CAD_history
0.347137 0.149195 0.581414 -0.004984 0.228735
Degrees of Freedom: 1001 Total (i.e. Null); 997 Residual
Null Deviance: 1338
Residual Deviance: 1311 AIC: 1321
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.9319 -1.2821 0.8263 0.9878 1.4773
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.693593 1.522867 0.455 0.6488
currentDF[, PROTEIN] 0.136420 0.068451 1.993 0.0463 *
Age -0.001134 0.008382 -0.135 0.8924
Gendermale 0.611644 0.146076 4.187 2.82e-05 ***
Hypertension.compositeyes -0.110197 0.204287 -0.539 0.5896
DiabetesStatusDiabetes -0.154479 0.162386 -0.951 0.3414
SmokerCurrentyes 0.113081 0.150460 0.752 0.4523
Med.Statin.LLDyes -0.148415 0.167690 -0.885 0.3761
Med.all.antiplateletyes 0.060511 0.223809 0.270 0.7869
GFR_MDRD -0.005323 0.003674 -1.449 0.1474
BMI 0.017774 0.018965 0.937 0.3486
CAD_history 0.289601 0.155217 1.866 0.0621 .
Stroke_history 0.153900 0.144197 1.067 0.2858
Peripheral.interv 0.004085 0.178235 0.023 0.9817
stenose50-70% -0.739569 1.207855 -0.612 0.5403
stenose70-90% -0.772461 1.182418 -0.653 0.5136
stenose90-99% -0.519486 1.182942 -0.439 0.6606
stenose100% (Occlusion) -0.605786 1.393142 -0.435 0.6637
stenose50-99% -1.096639 1.556511 -0.705 0.4811
stenose70-99% 0.852030 1.607425 0.530 0.5961
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1337.7 on 1001 degrees of freedom
Residual deviance: 1299.2 on 982 degrees of freedom
AIC: 1339.2
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_rank ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6R_pg_ug_2015_rank
Trait/outcome.............: IPH
Effect size...............: 0.13642
Standard error............: 0.068451
Odds ratio (effect size)..: 1.146
Lower 95% CI..............: 1.002
Upper 95% CI..............: 1.311
Z-value...................: 1.992945
P-value...................: 0.04626743
Hosmer and Lemeshow r^2...: 0.028723
Cox and Snell r^2.........: 0.037619
Nagelkerke's pseudo r^2...: 0.051055
Sample size of AE DB......: 2388
Sample size of model......: 1002
Missing data %............: 58.0402
Analysis of MCP1_pg_ug_2015_rank.
- processing CalcificationPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + SmokerCurrent + CAD_history + stenose, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Age SmokerCurrentyes CAD_history
-1.37299 -0.47406 0.02058 0.40394 0.24014
stenose50-70% stenose70-90% stenose90-99% stenose100% (Occlusion) stenose50-99%
-0.88314 -0.37300 -0.18504 0.71187 -14.80113
stenose70-99%
-1.21694
Degrees of Freedom: 1038 Total (i.e. Null); 1028 Residual
Null Deviance: 1439
Residual Deviance: 1354 AIC: 1376
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.8920 -1.0857 -0.6854 1.1100 2.0741
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -1.111475 1.329496 -0.836 0.40315
currentDF[, PROTEIN] -0.474349 0.068483 -6.927 4.31e-12 ***
Age 0.017814 0.008172 2.180 0.02927 *
Gendermale -0.141964 0.144258 -0.984 0.32507
Hypertension.compositeyes 0.223348 0.202733 1.102 0.27060
DiabetesStatusDiabetes -0.239143 0.160905 -1.486 0.13722
SmokerCurrentyes 0.415158 0.147167 2.821 0.00479 **
Med.Statin.LLDyes -0.180621 0.161314 -1.120 0.26285
Med.all.antiplateletyes -0.196875 0.219018 -0.899 0.36871
GFR_MDRD -0.001495 0.003520 -0.425 0.67110
BMI 0.012799 0.018157 0.705 0.48087
CAD_history 0.263913 0.150066 1.759 0.07864 .
Stroke_history -0.113515 0.140974 -0.805 0.42069
Peripheral.interv -0.192409 0.172486 -1.116 0.26463
stenose50-70% -0.816931 0.965795 -0.846 0.39763
stenose70-90% -0.342436 0.930578 -0.368 0.71289
stenose90-99% -0.154316 0.930195 -0.166 0.86824
stenose100% (Occlusion) 0.718982 1.255767 0.573 0.56695
stenose50-99% -14.791163 417.958374 -0.035 0.97177
stenose70-99% -1.145147 1.267273 -0.904 0.36619
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1439.2 on 1038 degrees of freedom
Residual deviance: 1345.5 on 1019 degrees of freedom
AIC: 1385.5
Number of Fisher Scoring iterations: 13
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: CalcificationPlaque
Effect size...............: -0.474349
Standard error............: 0.068483
Odds ratio (effect size)..: 0.622
Lower 95% CI..............: 0.544
Upper 95% CI..............: 0.712
Z-value...................: -6.926517
P-value...................: 4.313271e-12
Hosmer and Lemeshow r^2...: 0.065099
Cox and Snell r^2.........: 0.086227
Nagelkerke's pseudo r^2...: 0.115013
Sample size of AE DB......: 2388
Sample size of model......: 1039
Missing data %............: 56.49079
- processing CollagenPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
SmokerCurrent + BMI, family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] SmokerCurrentyes BMI
0.20733 -0.23235 0.44405 0.03906
Degrees of Freedom: 1041 Total (i.e. Null); 1038 Residual
Null Deviance: 1055
Residual Deviance: 1037 AIC: 1045
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.2648 0.4510 0.6178 0.7190 1.0442
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.346e+01 1.061e+03 0.013 0.98988
currentDF[, PROTEIN] -2.260e-01 7.941e-02 -2.846 0.00443 **
Age 1.026e-02 9.723e-03 1.055 0.29133
Gendermale 2.043e-02 1.727e-01 0.118 0.90585
Hypertension.compositeyes 2.367e-01 2.301e-01 1.029 0.30368
DiabetesStatusDiabetes 1.304e-01 1.981e-01 0.658 0.51029
SmokerCurrentyes 5.189e-01 1.836e-01 2.826 0.00472 **
Med.Statin.LLDyes 1.422e-02 1.924e-01 0.074 0.94111
Med.all.antiplateletyes 1.565e-01 2.563e-01 0.611 0.54149
GFR_MDRD 5.598e-03 4.241e-03 1.320 0.18688
BMI 4.070e-02 2.308e-02 1.763 0.07787 .
CAD_history 2.143e-01 1.838e-01 1.166 0.24353
Stroke_history 2.224e-01 1.727e-01 1.288 0.19786
Peripheral.interv 1.098e-01 2.127e-01 0.516 0.60574
stenose50-70% -1.453e+01 1.061e+03 -0.014 0.98907
stenose70-90% -1.499e+01 1.061e+03 -0.014 0.98873
stenose90-99% -1.505e+01 1.061e+03 -0.014 0.98869
stenose100% (Occlusion) 2.439e-01 1.351e+03 0.000 0.99986
stenose50-99% -1.824e-02 1.589e+03 0.000 0.99999
stenose70-99% -1.449e+01 1.061e+03 -0.014 0.98911
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1055.5 on 1041 degrees of freedom
Residual deviance: 1019.7 on 1022 degrees of freedom
AIC: 1059.7
Number of Fisher Scoring iterations: 15
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: CollagenPlaque
Effect size...............: -0.225966
Standard error............: 0.079411
Odds ratio (effect size)..: 0.798
Lower 95% CI..............: 0.683
Upper 95% CI..............: 0.932
Z-value...................: -2.845529
P-value...................: 0.00443377
Hosmer and Lemeshow r^2...: 0.033878
Cox and Snell r^2.........: 0.033733
Nagelkerke's pseudo r^2...: 0.052969
Sample size of AE DB......: 2388
Sample size of model......: 1042
Missing data %............: 56.36516
- processing Fat10Perc
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Gender + Stroke_history + Peripheral.interv + stenose, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Gendermale Stroke_history Peripheral.interv
13.9001 0.1685 0.8324 0.4438 -0.6179
stenose50-70% stenose70-90% stenose90-99% stenose100% (Occlusion) stenose50-99%
-13.6499 -13.5208 -13.3630 -14.0674 -15.9302
stenose70-99%
-14.7641
Degrees of Freedom: 1041 Total (i.e. Null); 1031 Residual
Null Deviance: 1225
Residual Deviance: 1154 AIC: 1176
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.1038 -1.1336 0.6504 0.8116 1.7658
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.325e+01 3.926e+02 0.034 0.973076
currentDF[, PROTEIN] 1.633e-01 7.323e-02 2.230 0.025771 *
Age 7.988e-03 8.972e-03 0.890 0.373294
Gendermale 8.706e-01 1.541e-01 5.650 1.6e-08 ***
Hypertension.compositeyes 4.406e-02 2.265e-01 0.194 0.845801
DiabetesStatusDiabetes -1.615e-01 1.756e-01 -0.920 0.357650
SmokerCurrentyes 1.448e-01 1.637e-01 0.885 0.376349
Med.Statin.LLDyes -1.876e-01 1.861e-01 -1.008 0.313562
Med.all.antiplateletyes 7.910e-02 2.423e-01 0.327 0.744039
GFR_MDRD -6.075e-04 3.926e-03 -0.155 0.877040
BMI 3.774e-03 1.972e-02 0.191 0.848251
CAD_history -3.938e-02 1.660e-01 -0.237 0.812467
Stroke_history 4.320e-01 1.633e-01 2.645 0.008160 **
Peripheral.interv -5.908e-01 1.790e-01 -3.300 0.000965 ***
stenose50-70% -1.358e+01 3.926e+02 -0.035 0.972398
stenose70-90% -1.345e+01 3.926e+02 -0.034 0.972660
stenose90-99% -1.331e+01 3.926e+02 -0.034 0.972956
stenose100% (Occlusion) -1.405e+01 3.926e+02 -0.036 0.971459
stenose50-99% -1.591e+01 3.926e+02 -0.041 0.967674
stenose70-99% -1.478e+01 3.926e+02 -0.038 0.969972
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1224.7 on 1041 degrees of freedom
Residual deviance: 1150.4 on 1022 degrees of freedom
AIC: 1190.4
Number of Fisher Scoring iterations: 13
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: Fat10Perc
Effect size...............: 0.163285
Standard error............: 0.073234
Odds ratio (effect size)..: 1.177
Lower 95% CI..............: 1.02
Upper 95% CI..............: 1.359
Z-value...................: 2.22964
P-value...................: 0.02577136
Hosmer and Lemeshow r^2...: 0.060666
Cox and Snell r^2.........: 0.068819
Nagelkerke's pseudo r^2...: 0.099553
Sample size of AE DB......: 2388
Sample size of model......: 1042
Missing data %............: 56.36516
- processing IPH
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Gender + Med.Statin.LLD + BMI + CAD_history + Stroke_history,
family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Gendermale Med.Statin.LLDyes BMI CAD_history
-0.57216 -0.13871 0.54948 -0.27048 0.02716 0.26512
Stroke_history
0.24072
Degrees of Freedom: 1039 Total (i.e. Null); 1033 Residual
Null Deviance: 1389
Residual Deviance: 1357 AIC: 1371
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.9631 -1.2808 0.8129 0.9902 1.5437
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.2859982 1.4840343 0.193 0.8472
currentDF[, PROTEIN] -0.1379693 0.0658565 -2.095 0.0362 *
Age 0.0001424 0.0081461 0.017 0.9860
Gendermale 0.6169887 0.1429940 4.315 1.6e-05 ***
Hypertension.compositeyes -0.1347125 0.2023482 -0.666 0.5056
DiabetesStatusDiabetes -0.1318405 0.1596422 -0.826 0.4089
SmokerCurrentyes 0.1353045 0.1476443 0.916 0.3594
Med.Statin.LLDyes -0.2582250 0.1645153 -1.570 0.1165
Med.all.antiplateletyes 0.1499145 0.2180924 0.687 0.4918
GFR_MDRD -0.0048941 0.0035333 -1.385 0.1660
BMI 0.0318061 0.0183315 1.735 0.0827 .
CAD_history 0.2693138 0.1520944 1.771 0.0766 .
Stroke_history 0.2396462 0.1427314 1.679 0.0932 .
Peripheral.interv 0.0531500 0.1737461 0.306 0.7597
stenose50-70% -0.9815249 1.1707234 -0.838 0.4018
stenose70-90% -0.8530624 1.1451377 -0.745 0.4563
stenose90-99% -0.5996543 1.1451261 -0.524 0.6005
stenose100% (Occlusion) -0.7495926 1.3619788 -0.550 0.5821
stenose50-99% -1.2353440 1.5277375 -0.809 0.4187
stenose70-99% 0.6521140 1.5833657 0.412 0.6804
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1388.6 on 1039 degrees of freedom
Residual deviance: 1344.4 on 1020 degrees of freedom
AIC: 1384.4
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: IPH
Effect size...............: -0.137969
Standard error............: 0.065856
Odds ratio (effect size)..: 0.871
Lower 95% CI..............: 0.766
Upper 95% CI..............: 0.991
Z-value...................: -2.095
P-value...................: 0.03617101
Hosmer and Lemeshow r^2...: 0.031873
Cox and Snell r^2.........: 0.041665
Nagelkerke's pseudo r^2...: 0.056541
Sample size of AE DB......: 2388
Sample size of model......: 1040
Missing data %............: 56.44891 cat("Edit the column names...\n")Edit the column names...colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
"Beta", "s.e.m.",
"OR", "low95CI", "up95CI",
"Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")
cat("Correct the variable types...\n")Correct the variable types...GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)
# Save the data
cat("Writing results to Excel-file...\n")Writing results to Excel-file...### Univariate
write.xlsx(GLM.results,
file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Multi.Protein.PlaquePhenotypes.RANK.MODEL2.xlsx"),
row.names = FALSE, col.names = TRUE, sheetName = "Bin.Multi.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")Removing intermediate files...rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)
Model 3
In this model we correct for Age, Gender, Hypertension status, Diabetes status, current smoker status, lipid-lowering drugs (LLDs), antiplatelet medication, eGFR (MDRD), BMI, CAD history, stroke history, peripheral interventions, stenosis, and LDL.
Natural log-transformed data
First we use the natural-log transformed data.
Analysis of continuous/quantitative plaque traits as a function of serum/plaque IL6(R)/MCP1 levels.
GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")Running linear regression...for (protein in 1:length(TRAITS.PROTEIN)) {
PROTEIN = TRAITS.PROTEIN[protein]
cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
for (trait in 1:length(TRAITS.CON)) {
TRAIT = TRAITS.CON[trait]
cat(paste0("\n- processing ",TRAIT,"\n\n"))
currentDF <- as.data.frame(AEDB.CEA %>%
dplyr::select(., PROTEIN, TRAIT, COVARIATES_M3) %>%
filter(complete.cases(.))) %>%
filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
# for debug
# print(DT::datatable(currentDF))
# print(nrow(currentDF))
# print(str(currentDF))
### univariate
fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender +
Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose + LDL_final,
data = currentDF)
model_step <- stepAIC(fit, direction = "both", trace = FALSE)
print(model_step)
print(summary(fit))
GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
GLM.results.TEMP[1,] = GLM.CON(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
GLM.results = rbind(GLM.results, GLM.results.TEMP)
}
}
Analysis of IL6_LN.
- processing Macrophages_LNNote: Using an external vector in selections is ambiguous.
[34mℹ[39m Use `all_of(COVARIATES_M3)` instead of `COVARIATES_M3` to silence this message.
[34mℹ[39m See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
[90mThis message is displayed once per session.[39m
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
4
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose +
LDL_final, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.9759 -0.7033 -0.0034 0.6905 4.2713
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.740947 1.579448 4.901 1.68e-06 ***
currentDF[, TRAIT] -0.025743 0.034058 -0.756 0.4504
Age -0.004751 0.009140 -0.520 0.6037
Gendermale -0.272080 0.157730 -1.725 0.0857 .
Hypertension.compositeyes 0.037631 0.209430 0.180 0.8575
DiabetesStatusDiabetes -0.090890 0.182293 -0.499 0.6185
SmokerCurrentyes 0.081890 0.146903 0.557 0.5777
Med.Statin.LLDyes -0.249783 0.162381 -1.538 0.1252
Med.all.antiplateletyes -0.239031 0.281515 -0.849 0.3966
GFR_MDRD 0.004160 0.004033 1.031 0.3033
BMI -0.010546 0.018853 -0.559 0.5764
CAD_history 0.175663 0.150932 1.164 0.2456
Stroke_history 0.078636 0.144235 0.545 0.5861
Peripheral.interv -0.149698 0.168586 -0.888 0.3754
stenose50-70% -2.622359 1.182113 -2.218 0.0274 *
stenose70-90% -2.484979 1.121013 -2.217 0.0275 *
stenose90-99% -2.587512 1.120320 -2.310 0.0217 *
stenose100% (Occlusion) -3.253230 1.268663 -2.564 0.0109 *
LDL_final -0.144230 0.071090 -2.029 0.0435 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.093 on 259 degrees of freedom
Multiple R-squared: 0.06051, Adjusted R-squared: -0.004781
F-statistic: 0.9268 on 18 and 259 DF, p-value: 0.5466
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_LN ' with ' Macrophages_LN ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_LN
Trait/outcome.............: Macrophages_LN
Effect size...............: -0.025743
Standard error............: 0.034058
Odds ratio (effect size)..: 0.975
Lower 95% CI..............: 0.912
Upper 95% CI..............: 1.042
T-value...................: -0.75585
P-value...................: 0.4504262
R^2.......................: 0.060512
Adjusted r^2..............: -0.004781
Sample size of AE DB......: 2388
Sample size of model......: 278
Missing data %............: 88.35846
- processing SMC_LN
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
4.02
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose +
LDL_final, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.0261 -0.6910 0.0053 0.6562 4.2703
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 8.045022 1.559939 5.157 4.94e-07 ***
currentDF[, TRAIT] 0.006083 0.044333 0.137 0.8910
Age -0.007760 0.008928 -0.869 0.3856
Gendermale -0.256524 0.155255 -1.652 0.0997 .
Hypertension.compositeyes -0.003596 0.198882 -0.018 0.9856
DiabetesStatusDiabetes -0.118621 0.178749 -0.664 0.5075
SmokerCurrentyes 0.002381 0.143389 0.017 0.9868
Med.Statin.LLDyes -0.219813 0.159597 -1.377 0.1696
Med.all.antiplateletyes -0.210099 0.279758 -0.751 0.4533
GFR_MDRD 0.003900 0.004023 0.969 0.3333
BMI -0.017825 0.018090 -0.985 0.3254
CAD_history 0.136209 0.149997 0.908 0.3647
Stroke_history 0.057146 0.141989 0.402 0.6877
Peripheral.interv -0.073426 0.169840 -0.432 0.6659
stenose50-70% -2.558450 1.175260 -2.177 0.0304 *
stenose70-90% -2.413938 1.114671 -2.166 0.0312 *
stenose90-99% -2.467474 1.112675 -2.218 0.0274 *
stenose100% (Occlusion) -3.172581 1.259270 -2.519 0.0123 *
LDL_final -0.119656 0.071964 -1.663 0.0976 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.086 on 263 degrees of freedom
Multiple R-squared: 0.05194, Adjusted R-squared: -0.01295
F-statistic: 0.8004 on 18 and 263 DF, p-value: 0.6994
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_LN ' with ' SMC_LN ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_LN
Trait/outcome.............: SMC_LN
Effect size...............: 0.006083
Standard error............: 0.044333
Odds ratio (effect size)..: 1.006
Lower 95% CI..............: 0.922
Upper 95% CI..............: 1.097
T-value...................: 0.137207
P-value...................: 0.8909723
R^2.......................: 0.051935
Adjusted r^2..............: -0.012951
Sample size of AE DB......: 2388
Sample size of model......: 282
Missing data %............: 88.19096
- processing VesselDensity_LN
Call:
lm(formula = currentDF[, PROTEIN] ~ GFR_MDRD + LDL_final, data = currentDF)
Coefficients:
(Intercept) GFR_MDRD LDL_final
3.925179 0.005269 -0.093708
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose +
LDL_final, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.9394 -0.6969 -0.0003 0.6700 4.3763
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 8.186084 1.618862 5.057 8.14e-07 ***
currentDF[, TRAIT] -0.071900 0.102862 -0.699 0.4852
Age -0.009714 0.009121 -1.065 0.2879
Gendermale -0.207723 0.157206 -1.321 0.1876
Hypertension.compositeyes 0.032540 0.206556 0.158 0.8749
DiabetesStatusDiabetes -0.093555 0.187795 -0.498 0.6188
SmokerCurrentyes 0.027341 0.147137 0.186 0.8527
Med.Statin.LLDyes -0.210534 0.161866 -1.301 0.1945
Med.all.antiplateletyes -0.127005 0.297437 -0.427 0.6697
GFR_MDRD 0.004613 0.004119 1.120 0.2638
BMI -0.017017 0.018372 -0.926 0.3552
CAD_history 0.158145 0.153266 1.032 0.3031
Stroke_history 0.070817 0.143918 0.492 0.6231
Peripheral.interv -0.087139 0.171114 -0.509 0.6110
stenose50-70% -2.634075 1.189079 -2.215 0.0276 *
stenose70-90% -2.461712 1.128624 -2.181 0.0301 *
stenose90-99% -2.542068 1.126823 -2.256 0.0249 *
stenose100% (Occlusion) -2.745285 1.332360 -2.060 0.0404 *
LDL_final -0.139731 0.071674 -1.950 0.0523 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.096 on 256 degrees of freedom
Multiple R-squared: 0.05537, Adjusted R-squared: -0.01105
F-statistic: 0.8337 on 18 and 256 DF, p-value: 0.6596
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_LN ' with ' VesselDensity_LN ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_LN
Trait/outcome.............: VesselDensity_LN
Effect size...............: -0.0719
Standard error............: 0.102862
Odds ratio (effect size)..: 0.931
Lower 95% CI..............: 0.761
Upper 95% CI..............: 1.138
T-value...................: -0.698998
P-value...................: 0.4851877
R^2.......................: 0.055374
Adjusted r^2..............: -0.011045
Sample size of AE DB......: 2388
Sample size of model......: 275
Missing data %............: 88.48409
Analysis of MCP1_LN.
- processing Macrophages_LN
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + Med.Statin.LLD + CAD_history,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age Gendermale Hypertension.compositeyes
5.433871 0.052907 -0.008419 0.283822 -0.246632
Med.Statin.LLDyes CAD_history
-0.245394 0.148189
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose +
LDL_final, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.3408 -0.5120 0.1051 0.5632 1.9179
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 6.575157 1.149922 5.718 2.42e-08 ***
currentDF[, TRAIT] 0.051202 0.024271 2.110 0.03565 *
Age -0.010344 0.006135 -1.686 0.09275 .
Gendermale 0.283093 0.108417 2.611 0.00944 **
Hypertension.compositeyes -0.233293 0.140032 -1.666 0.09667 .
DiabetesStatusDiabetes -0.052638 0.125203 -0.420 0.67445
SmokerCurrentyes -0.070866 0.100373 -0.706 0.48067
Med.Statin.LLDyes -0.200910 0.114627 -1.753 0.08058 .
Med.all.antiplateletyes 0.037672 0.198929 0.189 0.84992
GFR_MDRD -0.001374 0.002743 -0.501 0.61668
BMI -0.009776 0.012528 -0.780 0.43574
CAD_history 0.180157 0.106974 1.684 0.09311 .
Stroke_history 0.050938 0.100817 0.505 0.61372
Peripheral.interv -0.146160 0.121478 -1.203 0.22977
stenose50-70% -0.799688 0.914165 -0.875 0.38234
stenose70-90% -0.882827 0.875230 -1.009 0.31387
stenose90-99% -0.864213 0.875215 -0.987 0.32416
stenose100% (Occlusion) -1.846010 0.984886 -1.874 0.06177 .
LDL_final 0.059863 0.048325 1.239 0.21632
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8575 on 329 degrees of freedom
Multiple R-squared: 0.09616, Adjusted R-squared: 0.04671
F-statistic: 1.945 on 18 and 329 DF, p-value: 0.01237
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_LN ' with ' Macrophages_LN ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_LN
Trait/outcome.............: Macrophages_LN
Effect size...............: 0.051202
Standard error............: 0.024271
Odds ratio (effect size)..: 1.053
Lower 95% CI..............: 1.004
Upper 95% CI..............: 1.104
T-value...................: 2.10957
P-value...................: 0.03565039
R^2.......................: 0.096164
Adjusted r^2..............: 0.046714
Sample size of AE DB......: 2388
Sample size of model......: 348
Missing data %............: 85.42714
- processing SMC_LN
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + CAD_history + LDL_final,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age Gendermale Hypertension.compositeyes
4.91085 -0.11828 -0.01118 0.31926 -0.20731
CAD_history LDL_final
0.20467 0.14057
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose +
LDL_final, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.2139 -0.5490 0.0567 0.5531 1.8562
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 6.577965 1.115427 5.897 9.08e-09 ***
currentDF[, TRAIT] -0.115466 0.029928 -3.858 0.000137 ***
Age -0.014634 0.005906 -2.478 0.013716 *
Gendermale 0.293683 0.104952 2.798 0.005437 **
Hypertension.compositeyes -0.219243 0.132334 -1.657 0.098514 .
DiabetesStatusDiabetes -0.037367 0.121647 -0.307 0.758903
SmokerCurrentyes -0.061230 0.096647 -0.634 0.526814
Med.Statin.LLDyes -0.135829 0.110810 -1.226 0.221149
Med.all.antiplateletyes 0.015496 0.193719 0.080 0.936290
GFR_MDRD -0.001360 0.002667 -0.510 0.610531
BMI -0.006503 0.011921 -0.546 0.585774
CAD_history 0.229533 0.104305 2.201 0.028450 *
Stroke_history 0.072505 0.097399 0.744 0.457152
Peripheral.interv -0.141222 0.119345 -1.183 0.237528
stenose50-70% -0.850207 0.890508 -0.955 0.340400
stenose70-90% -0.964299 0.852952 -1.131 0.259061
stenose90-99% -0.945819 0.852072 -1.110 0.267791
stenose100% (Occlusion) -1.982611 0.958701 -2.068 0.039410 *
LDL_final 0.114283 0.048010 2.380 0.017856 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8357 on 333 degrees of freedom
Multiple R-squared: 0.1366, Adjusted R-squared: 0.0899
F-statistic: 2.926 on 18 and 333 DF, p-value: 7.408e-05
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_LN ' with ' SMC_LN ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_LN
Trait/outcome.............: SMC_LN
Effect size...............: -0.115466
Standard error............: 0.029928
Odds ratio (effect size)..: 0.891
Lower 95% CI..............: 0.84
Upper 95% CI..............: 0.945
T-value...................: -3.858177
P-value...................: 0.0001371672
R^2.......................: 0.136572
Adjusted r^2..............: 0.0899
Sample size of AE DB......: 2388
Sample size of model......: 352
Missing data %............: 85.25963
- processing VesselDensity_LN
Call:
lm(formula = currentDF[, PROTEIN] ~ Age + Gender + Hypertension.composite +
Med.Statin.LLD + CAD_history, data = currentDF)
Coefficients:
(Intercept) Age Gendermale Hypertension.compositeyes Med.Statin.LLDyes
5.411787 -0.009824 0.346534 -0.231090 -0.247329
CAD_history
0.171992
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose +
LDL_final, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.3025 -0.5216 0.0801 0.5662 1.9158
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 6.5990903 1.1774762 5.604 4.44e-08 ***
currentDF[, TRAIT] -0.0452791 0.0674970 -0.671 0.50280
Age -0.0109509 0.0060958 -1.796 0.07334 .
Gendermale 0.3402105 0.1089102 3.124 0.00195 **
Hypertension.compositeyes -0.2187995 0.1395317 -1.568 0.11783
DiabetesStatusDiabetes -0.0149288 0.1275963 -0.117 0.90693
SmokerCurrentyes -0.0647504 0.1007582 -0.643 0.52091
Med.Statin.LLDyes -0.2091982 0.1151857 -1.816 0.07026 .
Med.all.antiplateletyes 0.0262592 0.2037972 0.129 0.89756
GFR_MDRD -0.0004959 0.0027971 -0.177 0.85939
BMI -0.0081593 0.0124271 -0.657 0.51192
CAD_history 0.1953042 0.1090301 1.791 0.07417 .
Stroke_history 0.0600427 0.1009723 0.595 0.55249
Peripheral.interv -0.1222098 0.1239640 -0.986 0.32494
stenose50-70% -0.9307527 0.9190688 -1.013 0.31195
stenose70-90% -0.9661136 0.8811480 -1.096 0.27370
stenose90-99% -0.9411412 0.8803146 -1.069 0.28581
stenose100% (Occlusion) -1.9654537 0.9917186 -1.982 0.04833 *
LDL_final 0.0510253 0.0491298 1.039 0.29977
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8609 on 327 degrees of freedom
Multiple R-squared: 0.09072, Adjusted R-squared: 0.04067
F-statistic: 1.813 on 18 and 327 DF, p-value: 0.0229
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_LN ' with ' VesselDensity_LN ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_LN
Trait/outcome.............: VesselDensity_LN
Effect size...............: -0.045279
Standard error............: 0.067497
Odds ratio (effect size)..: 0.956
Lower 95% CI..............: 0.837
Upper 95% CI..............: 1.091
T-value...................: -0.670831
P-value...................: 0.5028019
R^2.......................: 0.090722
Adjusted r^2..............: 0.04067
Sample size of AE DB......: 2388
Sample size of model......: 346
Missing data %............: 85.51089
Analysis of IL6_pg_ug_2015_LN.
- processing Macrophages_LN
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + SmokerCurrent +
BMI + Stroke_history, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] SmokerCurrentyes BMI Stroke_history
-2.43206 0.09645 0.21278 -0.02995 0.32429
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose +
LDL_final, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-6.8604 -0.9047 0.0230 0.8359 4.8386
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.557747 1.218463 -1.278 0.20159
currentDF[, TRAIT] 0.094228 0.033001 2.855 0.00445 **
Age -0.005006 0.007368 -0.679 0.49711
Gendermale -0.046955 0.133198 -0.353 0.72458
Hypertension.compositeyes -0.122887 0.185203 -0.664 0.50725
DiabetesStatusDiabetes -0.095874 0.149517 -0.641 0.52163
SmokerCurrentyes 0.199220 0.129973 1.533 0.12586
Med.Statin.LLDyes -0.133702 0.151115 -0.885 0.37664
Med.all.antiplateletyes -0.032443 0.210849 -0.154 0.87777
GFR_MDRD -0.005327 0.003190 -1.670 0.09546 .
BMI -0.033230 0.016260 -2.044 0.04143 *
CAD_history -0.080693 0.137498 -0.587 0.55752
Stroke_history 0.294319 0.129450 2.274 0.02335 *
Peripheral.interv -0.073667 0.157291 -0.468 0.63971
stenose50-70% 0.043122 0.896119 0.048 0.96164
stenose70-90% 0.421831 0.854836 0.493 0.62187
stenose90-99% 0.194343 0.853986 0.228 0.82006
stenose100% (Occlusion) 0.199915 1.046672 0.191 0.84859
stenose70-99% -1.262844 1.353912 -0.933 0.35134
LDL_final -0.002720 0.060284 -0.045 0.96403
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.46 on 590 degrees of freedom
Multiple R-squared: 0.05558, Adjusted R-squared: 0.02517
F-statistic: 1.828 on 19 and 590 DF, p-value: 0.01729
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_LN ' with ' Macrophages_LN ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_pg_ug_2015_LN
Trait/outcome.............: Macrophages_LN
Effect size...............: 0.094228
Standard error............: 0.033001
Odds ratio (effect size)..: 1.099
Lower 95% CI..............: 1.03
Upper 95% CI..............: 1.172
T-value...................: 2.85531
P-value...................: 0.004450697
R^2.......................: 0.055584
Adjusted r^2..............: 0.025171
Sample size of AE DB......: 2388
Sample size of model......: 610
Missing data %............: 74.45561
- processing SMC_LN
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + SmokerCurrent +
BMI + Stroke_history, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] SmokerCurrentyes BMI Stroke_history
-2.70073 -0.16624 0.24106 -0.02353 0.27785
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose +
LDL_final, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-6.5367 -0.9049 0.0226 0.8649 4.3823
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.655561 1.203986 -1.375 0.1696
currentDF[, TRAIT] -0.172293 0.041530 -4.149 3.83e-05 ***
Age -0.010475 0.007294 -1.436 0.1515
Gendermale -0.080710 0.132821 -0.608 0.5436
Hypertension.compositeyes -0.059646 0.180561 -0.330 0.7413
DiabetesStatusDiabetes -0.078541 0.148335 -0.529 0.5967
SmokerCurrentyes 0.195491 0.128526 1.521 0.1288
Med.Statin.LLDyes -0.128654 0.148731 -0.865 0.3874
Med.all.antiplateletyes -0.047780 0.207507 -0.230 0.8180
GFR_MDRD -0.004567 0.003173 -1.440 0.1505
BMI -0.027125 0.015913 -1.705 0.0888 .
CAD_history -0.079941 0.136513 -0.586 0.5584
Stroke_history 0.268793 0.128036 2.099 0.0362 *
Peripheral.interv -0.051546 0.157517 -0.327 0.7436
stenose50-70% 0.064469 0.888583 0.073 0.9422
stenose70-90% 0.480430 0.847963 0.567 0.5712
stenose90-99% 0.261853 0.846882 0.309 0.7573
stenose100% (Occlusion) 0.357215 1.010357 0.354 0.7238
stenose70-99% -0.949383 1.343326 -0.707 0.4800
LDL_final 0.021597 0.060074 0.360 0.7193
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.448 on 593 degrees of freedom
Multiple R-squared: 0.06707, Adjusted R-squared: 0.03717
F-statistic: 2.244 on 19 and 593 DF, p-value: 0.001915
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_LN ' with ' SMC_LN ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_pg_ug_2015_LN
Trait/outcome.............: SMC_LN
Effect size...............: -0.172293
Standard error............: 0.04153
Odds ratio (effect size)..: 0.842
Lower 95% CI..............: 0.776
Upper 95% CI..............: 0.913
T-value...................: -4.148622
P-value...................: 3.834173e-05
R^2.......................: 0.067066
Adjusted r^2..............: 0.037174
Sample size of AE DB......: 2388
Sample size of model......: 613
Missing data %............: 74.32998
- processing VesselDensity_LN
Call:
lm(formula = currentDF[, PROTEIN] ~ SmokerCurrent + GFR_MDRD +
BMI + Stroke_history, data = currentDF)
Coefficients:
(Intercept) SmokerCurrentyes GFR_MDRD BMI Stroke_history
-2.344005 0.259757 -0.004498 -0.028325 0.352416
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose +
LDL_final, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-6.7750 -0.8639 0.0090 0.8596 4.6815
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.790409 1.260654 -1.420 0.1561
currentDF[, TRAIT] -0.066015 0.076053 -0.868 0.3858
Age -0.003151 0.007664 -0.411 0.6812
Gendermale -0.009092 0.138905 -0.065 0.9478
Hypertension.compositeyes -0.118670 0.194865 -0.609 0.5428
DiabetesStatusDiabetes -0.044101 0.160394 -0.275 0.7835
SmokerCurrentyes 0.234773 0.136162 1.724 0.0852 .
Med.Statin.LLDyes -0.119714 0.158225 -0.757 0.4496
Med.all.antiplateletyes -0.042429 0.229876 -0.185 0.8536
GFR_MDRD -0.005754 0.003392 -1.696 0.0904 .
BMI -0.029793 0.016765 -1.777 0.0761 .
CAD_history -0.091576 0.146623 -0.625 0.5325
Stroke_history 0.324994 0.136565 2.380 0.0177 *
Peripheral.interv -0.092246 0.169383 -0.545 0.5863
stenose50-70% -0.074196 0.908935 -0.082 0.9350
stenose70-90% 0.295609 0.863719 0.342 0.7323
stenose90-99% 0.102435 0.862536 0.119 0.9055
stenose100% (Occlusion) 0.128583 1.056678 0.122 0.9032
stenose70-99% -1.344224 1.735406 -0.775 0.4389
LDL_final 0.016792 0.065246 0.257 0.7970
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.471 on 542 degrees of freedom
Multiple R-squared: 0.04595, Adjusted R-squared: 0.01251
F-statistic: 1.374 on 19 and 542 DF, p-value: 0.1332
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_LN ' with ' VesselDensity_LN ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_pg_ug_2015_LN
Trait/outcome.............: VesselDensity_LN
Effect size...............: -0.066015
Standard error............: 0.076053
Odds ratio (effect size)..: 0.936
Lower 95% CI..............: 0.806
Upper 95% CI..............: 1.087
T-value...................: -0.868014
P-value...................: 0.3857707
R^2.......................: 0.045953
Adjusted r^2..............: 0.012509
Sample size of AE DB......: 2388
Sample size of model......: 562
Missing data %............: 76.46566
Analysis of IL6R_pg_ug_2015_LN.
- processing Macrophages_LN
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
DiabetesStatus + Med.Statin.LLD + GFR_MDRD + Peripheral.interv,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age DiabetesStatusDiabetes Med.Statin.LLDyes
-0.758425 0.061200 -0.007761 -0.173464 -0.263146
GFR_MDRD Peripheral.interv
-0.004980 0.315321
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose +
LDL_final, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-7.5997 -0.4916 0.1033 0.6403 2.7135
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.282532 1.033781 -0.273 0.78472
currentDF[, TRAIT] 0.064192 0.024826 2.586 0.00996 **
Age -0.007926 0.005731 -1.383 0.16718
Gendermale -0.070614 0.101607 -0.695 0.48735
Hypertension.compositeyes 0.024561 0.138756 0.177 0.85956
DiabetesStatusDiabetes -0.167687 0.115198 -1.456 0.14603
SmokerCurrentyes 0.006786 0.099727 0.068 0.94577
Med.Statin.LLDyes -0.258587 0.116721 -2.215 0.02712 *
Med.all.antiplateletyes -0.161527 0.163593 -0.987 0.32387
GFR_MDRD -0.004904 0.002499 -1.962 0.05024 .
BMI -0.017520 0.012739 -1.375 0.16955
CAD_history -0.055607 0.105034 -0.529 0.59672
Stroke_history 0.056941 0.099170 0.574 0.56607
Peripheral.interv 0.298235 0.120881 2.467 0.01390 *
stenose50-70% -0.144222 0.822263 -0.175 0.86083
stenose70-90% 0.098077 0.795684 0.123 0.90194
stenose90-99% 0.174778 0.794935 0.220 0.82605
stenose100% (Occlusion) -0.277654 0.917836 -0.303 0.76237
stenose70-99% -1.386821 1.125420 -1.232 0.21834
LDL_final 0.016695 0.046789 0.357 0.72136
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.109 on 584 degrees of freedom
Multiple R-squared: 0.06059, Adjusted R-squared: 0.03002
F-statistic: 1.982 on 19 and 584 DF, p-value: 0.007875
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_LN ' with ' Macrophages_LN ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6R_pg_ug_2015_LN
Trait/outcome.............: Macrophages_LN
Effect size...............: 0.064192
Standard error............: 0.024826
Odds ratio (effect size)..: 1.066
Lower 95% CI..............: 1.016
Upper 95% CI..............: 1.119
T-value...................: 2.585637
P-value...................: 0.009960997
R^2.......................: 0.060586
Adjusted r^2..............: 0.030023
Sample size of AE DB......: 2388
Sample size of model......: 604
Missing data %............: 74.70687
- processing SMC_LN
Call:
lm(formula = currentDF[, PROTEIN] ~ Age + DiabetesStatus + Med.Statin.LLD +
GFR_MDRD + Peripheral.interv, data = currentDF)
Coefficients:
(Intercept) Age DiabetesStatusDiabetes Med.Statin.LLDyes GFR_MDRD
-0.645427 -0.010345 -0.159295 -0.283019 -0.004953
Peripheral.interv
0.307515
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose +
LDL_final, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-7.6508 -0.4774 0.1193 0.6125 2.7379
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.571572 1.028428 -0.556 0.5786
currentDF[, TRAIT] 0.028425 0.032160 0.884 0.3771
Age -0.009415 0.005714 -1.648 0.0999 .
Gendermale -0.009854 0.101751 -0.097 0.9229
Hypertension.compositeyes 0.047185 0.136267 0.346 0.7293
DiabetesStatusDiabetes -0.159308 0.115018 -1.385 0.1666
SmokerCurrentyes 0.018075 0.099297 0.182 0.8556
Med.Statin.LLDyes -0.265016 0.115598 -2.293 0.0222 *
Med.all.antiplateletyes -0.144961 0.162054 -0.895 0.3714
GFR_MDRD -0.004824 0.002503 -1.928 0.0544 .
BMI -0.010965 0.012533 -0.875 0.3820
CAD_history -0.071840 0.105008 -0.684 0.4942
Stroke_history 0.082184 0.098712 0.833 0.4054
Peripheral.interv 0.299575 0.121917 2.457 0.0143 *
stenose50-70% -0.129023 0.821241 -0.157 0.8752
stenose70-90% 0.126408 0.794746 0.159 0.8737
stenose90-99% 0.210391 0.793876 0.265 0.7911
stenose100% (Occlusion) -0.293635 0.899684 -0.326 0.7443
stenose70-99% -1.262022 1.124404 -1.122 0.2622
LDL_final 0.023878 0.046958 0.508 0.6113
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.107 on 587 degrees of freedom
Multiple R-squared: 0.05271, Adjusted R-squared: 0.02205
F-statistic: 1.719 on 19 and 587 DF, p-value: 0.02935
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_LN ' with ' SMC_LN ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6R_pg_ug_2015_LN
Trait/outcome.............: SMC_LN
Effect size...............: 0.028425
Standard error............: 0.03216
Odds ratio (effect size)..: 1.029
Lower 95% CI..............: 0.966
Upper 95% CI..............: 1.096
T-value...................: 0.88386
P-value...................: 0.3771337
R^2.......................: 0.052709
Adjusted r^2..............: 0.022047
Sample size of AE DB......: 2388
Sample size of model......: 607
Missing data %............: 74.58124
- processing VesselDensity_LN
Call:
lm(formula = currentDF[, PROTEIN] ~ Age + Med.Statin.LLD + GFR_MDRD +
Peripheral.interv, data = currentDF)
Coefficients:
(Intercept) Age Med.Statin.LLDyes GFR_MDRD Peripheral.interv
-0.623263 -0.010168 -0.321089 -0.005272 0.269837
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose +
LDL_final, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-7.6588 -0.4881 0.1029 0.6320 2.6591
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.409939 1.076240 -0.381 0.7034
currentDF[, TRAIT] 0.051036 0.060112 0.849 0.3963
Age -0.010250 0.006021 -1.702 0.0892 .
Gendermale -0.023004 0.107103 -0.215 0.8300
Hypertension.compositeyes -0.013676 0.148086 -0.092 0.9265
DiabetesStatusDiabetes -0.139140 0.125480 -1.109 0.2680
SmokerCurrentyes -0.031038 0.105613 -0.294 0.7690
Med.Statin.LLDyes -0.289430 0.123728 -2.339 0.0197 *
Med.all.antiplateletyes -0.128154 0.180604 -0.710 0.4783
GFR_MDRD -0.005528 0.002700 -2.048 0.0411 *
BMI -0.014719 0.013397 -1.099 0.2724
CAD_history -0.068964 0.113307 -0.609 0.5430
Stroke_history 0.043365 0.105700 0.410 0.6818
Peripheral.interv 0.268876 0.131623 2.043 0.0416 *
stenose50-70% -0.129415 0.840865 -0.154 0.8777
stenose70-90% 0.154123 0.810665 0.190 0.8493
stenose90-99% 0.225741 0.809735 0.279 0.7805
stenose100% (Occlusion) -0.238605 0.935267 -0.255 0.7987
stenose70-99% -1.686131 1.410283 -1.196 0.2324
LDL_final 0.034234 0.051289 0.667 0.5048
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.128 on 534 degrees of freedom
Multiple R-squared: 0.05257, Adjusted R-squared: 0.01886
F-statistic: 1.559 on 19 and 534 DF, p-value: 0.06155
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_LN ' with ' VesselDensity_LN ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6R_pg_ug_2015_LN
Trait/outcome.............: VesselDensity_LN
Effect size...............: 0.051036
Standard error............: 0.060112
Odds ratio (effect size)..: 1.052
Lower 95% CI..............: 0.935
Upper 95% CI..............: 1.184
T-value...................: 0.849019
P-value...................: 0.396251
R^2.......................: 0.052566
Adjusted r^2..............: 0.018856
Sample size of AE DB......: 2388
Sample size of model......: 554
Missing data %............: 76.80067
Analysis of MCP1_pg_ug_2015_LN.
- processing Macrophages_LN
Call:
lm(formula = currentDF[, PROTEIN] ~ Gender + Hypertension.composite +
Stroke_history + LDL_final, data = currentDF)
Coefficients:
(Intercept) Gendermale Hypertension.compositeyes Stroke_history LDL_final
-1.5707 0.1726 -0.3481 0.3069 0.1504
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose +
LDL_final, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-5.0660 -0.7930 0.0067 0.8502 3.5632
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.929404 1.105948 -0.840 0.40103
currentDF[, TRAIT] -0.006916 0.029359 -0.236 0.81385
Age -0.003860 0.006678 -0.578 0.56345
Gendermale 0.177357 0.119206 1.488 0.13732
Hypertension.compositeyes -0.333981 0.164211 -2.034 0.04240 *
DiabetesStatusDiabetes -0.088384 0.134409 -0.658 0.51106
SmokerCurrentyes -0.072973 0.116824 -0.625 0.53244
Med.Statin.LLDyes -0.183941 0.136585 -1.347 0.17858
Med.all.antiplateletyes -0.177579 0.188679 -0.941 0.34700
GFR_MDRD -0.003332 0.002879 -1.157 0.24760
BMI -0.011705 0.014568 -0.803 0.42202
CAD_history 0.075332 0.122823 0.613 0.53988
Stroke_history 0.317516 0.116987 2.714 0.00683 **
Peripheral.interv 0.064670 0.141725 0.456 0.64833
stenose50-70% 0.194101 0.815272 0.238 0.81190
stenose70-90% 0.607022 0.777643 0.781 0.43535
stenose90-99% 0.447575 0.776967 0.576 0.56479
stenose100% (Occlusion) -0.665847 0.952000 -0.699 0.48456
stenose70-99% 0.775391 1.231567 0.630 0.52920
LDL_final 0.135784 0.054445 2.494 0.01290 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.328 on 606 degrees of freedom
Multiple R-squared: 0.05948, Adjusted R-squared: 0.02999
F-statistic: 2.017 on 19 and 606 DF, p-value: 0.006522
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_LN ' with ' Macrophages_LN ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_LN
Trait/outcome.............: Macrophages_LN
Effect size...............: -0.006916
Standard error............: 0.029359
Odds ratio (effect size)..: 0.993
Lower 95% CI..............: 0.938
Upper 95% CI..............: 1.052
T-value...................: -0.235562
P-value...................: 0.8138524
R^2.......................: 0.059478
Adjusted r^2..............: 0.029989
Sample size of AE DB......: 2388
Sample size of model......: 626
Missing data %............: 73.7856
- processing SMC_LN
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Hypertension.composite +
Stroke_history + LDL_final, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Hypertension.compositeyes Stroke_history LDL_final
-1.4877 -0.1145 -0.3259 0.2511 0.1689
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose +
LDL_final, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-4.8298 -0.7851 -0.0041 0.8556 3.5190
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.959118 1.098926 -0.873 0.38313
currentDF[, TRAIT] -0.105998 0.037524 -2.825 0.00489 **
Age -0.005301 0.006643 -0.798 0.42518
Gendermale 0.146474 0.119488 1.226 0.22073
Hypertension.compositeyes -0.310950 0.161119 -1.930 0.05408 .
DiabetesStatusDiabetes -0.126107 0.134058 -0.941 0.34724
SmokerCurrentyes -0.073389 0.116179 -0.632 0.52783
Med.Statin.LLDyes -0.180246 0.135211 -1.333 0.18301
Med.all.antiplateletyes -0.197609 0.186793 -1.058 0.29052
GFR_MDRD -0.002675 0.002879 -0.929 0.35322
BMI -0.010517 0.014344 -0.733 0.46372
CAD_history 0.067123 0.122608 0.547 0.58426
Stroke_history 0.276835 0.116332 2.380 0.01763 *
Peripheral.interv 0.115307 0.142703 0.808 0.41940
stenose50-70% 0.224709 0.812884 0.276 0.78231
stenose70-90% 0.642432 0.775621 0.828 0.40784
stenose90-99% 0.513547 0.774793 0.663 0.50770
stenose100% (Occlusion) -0.304064 0.924098 -0.329 0.74224
stenose70-99% 0.907983 1.228759 0.739 0.46023
LDL_final 0.154355 0.054558 2.829 0.00482 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.324 on 609 degrees of freedom
Multiple R-squared: 0.07024, Adjusted R-squared: 0.04124
F-statistic: 2.422 on 19 and 609 DF, p-value: 0.0006923
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_LN ' with ' SMC_LN ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_LN
Trait/outcome.............: SMC_LN
Effect size...............: -0.105998
Standard error............: 0.037524
Odds ratio (effect size)..: 0.899
Lower 95% CI..............: 0.836
Upper 95% CI..............: 0.968
T-value...................: -2.824808
P-value...................: 0.004885846
R^2.......................: 0.070244
Adjusted r^2..............: 0.041237
Sample size of AE DB......: 2388
Sample size of model......: 629
Missing data %............: 73.65997
- processing VesselDensity_LN
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
Hypertension.composite + Stroke_history + LDL_final, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale Hypertension.compositeyes Stroke_history
-1.5191 -0.1567 0.2528 -0.2838 0.3058
LDL_final
0.1790
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose +
LDL_final, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-4.8851 -0.7703 0.0065 0.8631 3.5802
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.523924 1.144057 -0.458 0.6472
currentDF[, TRAIT] -0.150564 0.068731 -2.191 0.0289 *
Age -0.004199 0.006947 -0.604 0.5458
Gendermale 0.247989 0.124551 1.991 0.0470 *
Hypertension.compositeyes -0.260669 0.174608 -1.493 0.1360
DiabetesStatusDiabetes -0.080503 0.144545 -0.557 0.5778
SmokerCurrentyes -0.038813 0.122667 -0.316 0.7518
Med.Statin.LLDyes -0.172549 0.143615 -1.201 0.2301
Med.all.antiplateletyes -0.151041 0.205188 -0.736 0.4620
GFR_MDRD -0.003511 0.003061 -1.147 0.2519
BMI -0.018790 0.015167 -1.239 0.2159
CAD_history 0.081761 0.131532 0.622 0.5345
Stroke_history 0.312108 0.123405 2.529 0.0117 *
Peripheral.interv 0.010985 0.152518 0.072 0.9426
stenose50-70% -0.056253 0.827227 -0.068 0.9458
stenose70-90% 0.437868 0.785986 0.557 0.5777
stenose90-99% 0.309260 0.785037 0.394 0.6938
stenose100% (Occlusion) -0.769104 0.961372 -0.800 0.4240
stenose70-99% 0.904530 1.579083 0.573 0.5670
LDL_final 0.159352 0.058973 2.702 0.0071 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.339 on 555 degrees of freedom
Multiple R-squared: 0.07268, Adjusted R-squared: 0.04094
F-statistic: 2.29 on 19 and 555 DF, p-value: 0.001509
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_LN ' with ' VesselDensity_LN ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_LN
Trait/outcome.............: VesselDensity_LN
Effect size...............: -0.150564
Standard error............: 0.068731
Odds ratio (effect size)..: 0.86
Lower 95% CI..............: 0.752
Upper 95% CI..............: 0.984
T-value...................: -2.190616
P-value...................: 0.0288948
R^2.......................: 0.072685
Adjusted r^2..............: 0.040939
Sample size of AE DB......: 2388
Sample size of model......: 575
Missing data %............: 75.92127 cat("Edit the column names...\n")Edit the column names...colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
"Beta", "s.e.m.",
"OR", "low95CI", "up95CI",
"T-value", "P-value", "r^2", "r^2_adj", "AE_N", "Model_N", "Perc_Miss")
cat("Correct the variable types...\n")Correct the variable types...GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)
# Save the data
cat("Writing results to Excel-file...\n")Writing results to Excel-file...### Univariate
library(openxlsx)
write.xlsx(GLM.results,
file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Con.Multi.Protein.PlaquePhenotypes.MODEL3.xlsx"),
row.names = FALSE, col.names = TRUE, sheetName = "Con.Multi.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")Removing intermediate files...rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)
Analysis of binary plaque traits as a function of serum/plaque IL6(R)/MCP1 levels.
GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN)) {
PROTEIN = TRAITS.PROTEIN[protein]
cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
for (trait in 1:length(TRAITS.BIN)) {
TRAIT = TRAITS.BIN[trait]
cat(paste0("\n- processing ",TRAIT,"\n\n"))
currentDF <- as.data.frame(AEDB.CEA %>%
dplyr::select(., PROTEIN, TRAIT, COVARIATES_M3) %>%
filter(complete.cases(.))) %>%
filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
# for debug
# print(DT::datatable(currentDF))
# print(nrow(currentDF))
# print(str(currentDF))
# print(class(currentDF[,TRAIT]))
### univariate
fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender +
Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose + LDL_final,
data = currentDF, family = binomial(link = "logit"))
model_step <- stepAIC(fit, direction = "both", trace = FALSE)
print(model_step)
print(summary(fit))
GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
GLM.results = rbind(GLM.results, GLM.results.TEMP)
}
}
Analysis of IL6_LN.
- processing CalcificationPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Hypertension.composite,
family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) Hypertension.compositeyes
-0.4964 0.8687
Degrees of Freedom: 286 Total (i.e. Null); 285 Residual
Null Deviance: 393.1
Residual Deviance: 387.1 AIC: 391.1
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + LDL_final, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.8203 -1.2203 0.7912 1.0267 1.6584
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -14.286196 882.746445 -0.016 0.9871
currentDF[, PROTEIN] 0.127711 0.117619 1.086 0.2776
Age -0.007405 0.017177 -0.431 0.6664
Gendermale -0.107807 0.294894 -0.366 0.7147
Hypertension.compositeyes 0.943344 0.388474 2.428 0.0152 *
DiabetesStatusDiabetes -0.282098 0.340209 -0.829 0.4070
SmokerCurrentyes -0.158885 0.271685 -0.585 0.5587
Med.Statin.LLDyes -0.387047 0.306268 -1.264 0.2063
Med.all.antiplateletyes 0.693695 0.541645 1.281 0.2003
GFR_MDRD -0.009341 0.007815 -1.195 0.2320
BMI -0.009798 0.034417 -0.285 0.7759
CAD_history 0.267935 0.287363 0.932 0.3511
Stroke_history -0.132062 0.271241 -0.487 0.6263
Peripheral.interv -0.396651 0.313421 -1.266 0.2057
stenose50-70% 15.622260 882.743866 0.018 0.9859
stenose70-90% 15.078260 882.743546 0.017 0.9864
stenose90-99% 14.713152 882.743540 0.017 0.9867
stenose100% (Occlusion) 16.300162 882.744474 0.018 0.9853
LDL_final -0.134622 0.134950 -0.998 0.3185
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 393.08 on 286 degrees of freedom
Residual deviance: 372.59 on 268 degrees of freedom
AIC: 410.59
Number of Fisher Scoring iterations: 13
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_LN ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_LN
Trait/outcome.............: CalcificationPlaque
Effect size...............: 0.127711
Standard error............: 0.117619
Odds ratio (effect size)..: 1.136
Lower 95% CI..............: 0.902
Upper 95% CI..............: 1.431
Z-value...................: 1.085796
P-value...................: 0.2775691
Hosmer and Lemeshow r^2...: 0.052141
Cox and Snell r^2.........: 0.068924
Nagelkerke's pseudo r^2...: 0.092416
Sample size of AE DB......: 2388
Sample size of model......: 287
Missing data %............: 87.98158
- processing CollagenPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Med.all.antiplatelet +
GFR_MDRD + stenose, family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) Med.all.antiplateletyes GFR_MDRD stenose50-70% stenose70-90%
17.50951 0.82920 -0.01174 0.26966 -16.02111
stenose90-99% stenose100% (Occlusion)
-16.41110 0.85461
Degrees of Freedom: 285 Total (i.e. Null); 279 Residual
Null Deviance: 304.1
Residual Deviance: 291.8 AIC: 305.8
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + LDL_final, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.00491 0.00023 0.63115 0.76535 1.18759
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.755e+01 3.956e+03 0.004 0.996
currentDF[, PROTEIN] 6.100e-02 1.397e-01 0.437 0.662
Age 8.498e-03 2.038e-02 0.417 0.677
Gendermale -2.791e-01 3.615e-01 -0.772 0.440
Hypertension.compositeyes 4.528e-01 4.345e-01 1.042 0.297
DiabetesStatusDiabetes 4.050e-01 4.292e-01 0.944 0.345
SmokerCurrentyes 3.410e-01 3.259e-01 1.046 0.295
Med.Statin.LLDyes -2.690e-01 3.648e-01 -0.737 0.461
Med.all.antiplateletyes 8.442e-01 5.714e-01 1.477 0.140
GFR_MDRD -1.028e-02 9.278e-03 -1.108 0.268
BMI -3.652e-02 4.094e-02 -0.892 0.372
CAD_history -1.245e-02 3.377e-01 -0.037 0.971
Stroke_history 2.234e-01 3.336e-01 0.670 0.503
Peripheral.interv -2.317e-01 3.598e-01 -0.644 0.519
stenose50-70% 1.679e-01 4.163e+03 0.000 1.000
stenose70-90% -1.614e+01 3.956e+03 -0.004 0.997
stenose90-99% -1.661e+01 3.956e+03 -0.004 0.997
stenose100% (Occlusion) 6.788e-01 4.417e+03 0.000 1.000
LDL_final -1.338e-02 1.608e-01 -0.083 0.934
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 304.10 on 285 degrees of freedom
Residual deviance: 285.69 on 267 degrees of freedom
AIC: 323.69
Number of Fisher Scoring iterations: 16
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_LN ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_LN
Trait/outcome.............: CollagenPlaque
Effect size...............: 0.060996
Standard error............: 0.139671
Odds ratio (effect size)..: 1.063
Lower 95% CI..............: 0.808
Upper 95% CI..............: 1.398
Z-value...................: 0.43671
P-value...................: 0.6623216
Hosmer and Lemeshow r^2...: 0.060544
Cox and Snell r^2.........: 0.062348
Nagelkerke's pseudo r^2...: 0.095233
Sample size of AE DB......: 2388
Sample size of model......: 286
Missing data %............: 88.02345
- processing Fat10Perc
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Gender + Hypertension.composite,
family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) Gendermale Hypertension.compositeyes
0.1798 0.6324 1.0185
Degrees of Freedom: 286 Total (i.e. Null); 284 Residual
Null Deviance: 277.5
Residual Deviance: 267.3 AIC: 273.3
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + LDL_final, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.4640 0.3869 0.5163 0.6631 1.2321
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.331e+01 2.400e+03 0.006 0.9956
currentDF[, PROTEIN] 5.848e-02 1.460e-01 0.401 0.6887
Age -6.607e-03 2.259e-02 -0.292 0.7699
Gendermale 8.909e-01 3.638e-01 2.449 0.0143 *
Hypertension.compositeyes 1.129e+00 4.236e-01 2.665 0.0077 **
DiabetesStatusDiabetes -1.347e-01 4.364e-01 -0.309 0.7577
SmokerCurrentyes 3.119e-01 3.609e-01 0.864 0.3875
Med.Statin.LLDyes 7.371e-02 3.929e-01 0.188 0.8512
Med.all.antiplateletyes 6.757e-01 6.391e-01 1.057 0.2904
GFR_MDRD -8.549e-03 1.040e-02 -0.822 0.4111
BMI 3.147e-02 4.539e-02 0.693 0.4881
CAD_history -2.786e-02 3.702e-01 -0.075 0.9400
Stroke_history 2.389e-01 3.632e-01 0.658 0.5107
Peripheral.interv -3.988e-01 3.797e-01 -1.050 0.2937
stenose50-70% -1.608e+01 2.400e+03 -0.007 0.9947
stenose70-90% -1.460e+01 2.400e+03 -0.006 0.9951
stenose90-99% -1.495e+01 2.400e+03 -0.006 0.9950
stenose100% (Occlusion) 9.700e-01 2.666e+03 0.000 0.9997
LDL_final 2.326e-01 1.886e-01 1.233 0.2176
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 277.55 on 286 degrees of freedom
Residual deviance: 254.25 on 268 degrees of freedom
AIC: 292.25
Number of Fisher Scoring iterations: 15
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_LN ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_LN
Trait/outcome.............: Fat10Perc
Effect size...............: 0.058484
Standard error............: 0.146006
Odds ratio (effect size)..: 1.06
Lower 95% CI..............: 0.796
Upper 95% CI..............: 1.412
Z-value...................: 0.400559
P-value...................: 0.6887448
Hosmer and Lemeshow r^2...: 0.083927
Cox and Snell r^2.........: 0.077957
Nagelkerke's pseudo r^2...: 0.125777
Sample size of AE DB......: 2388
Sample size of model......: 287
Missing data %............: 87.98158
- processing IPH
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + Hypertension.composite +
DiabetesStatus, family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) Age Hypertension.compositeyes DiabetesStatusDiabetes
-1.60096 0.03481 0.61722 -0.58861
Degrees of Freedom: 286 Total (i.e. Null); 283 Residual
Null Deviance: 323.3
Residual Deviance: 313.4 AIC: 321.4
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + LDL_final, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.9750 -0.5510 0.6402 0.7877 1.2400
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 11.218363 882.747304 0.013 0.9899
currentDF[, PROTEIN] -0.088057 0.127517 -0.691 0.4898
Age 0.034226 0.019081 1.794 0.0729 .
Gendermale 0.371138 0.322752 1.150 0.2502
Hypertension.compositeyes 0.644421 0.400128 1.611 0.1073
DiabetesStatusDiabetes -0.578214 0.362096 -1.597 0.1103
SmokerCurrentyes 0.176265 0.309514 0.569 0.5690
Med.Statin.LLDyes 0.045481 0.344011 0.132 0.8948
Med.all.antiplateletyes -0.109971 0.637921 -0.172 0.8631
GFR_MDRD -0.002540 0.008826 -0.288 0.7735
BMI 0.007721 0.038101 0.203 0.8394
CAD_history 0.066670 0.328960 0.203 0.8394
Stroke_history 0.118372 0.311575 0.380 0.7040
Peripheral.interv 0.143803 0.361519 0.398 0.6908
stenose50-70% -12.563912 882.744016 -0.014 0.9886
stenose70-90% -12.969217 882.743594 -0.015 0.9883
stenose90-99% -13.023753 882.743587 -0.015 0.9882
stenose100% (Occlusion) -12.810939 882.744556 -0.015 0.9884
LDL_final 0.054494 0.155443 0.351 0.7259
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 323.33 on 286 degrees of freedom
Residual deviance: 310.07 on 268 degrees of freedom
AIC: 348.07
Number of Fisher Scoring iterations: 13
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_LN ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_LN
Trait/outcome.............: IPH
Effect size...............: -0.088057
Standard error............: 0.127517
Odds ratio (effect size)..: 0.916
Lower 95% CI..............: 0.713
Upper 95% CI..............: 1.176
Z-value...................: -0.69055
P-value...................: 0.4898483
Hosmer and Lemeshow r^2...: 0.041014
Cox and Snell r^2.........: 0.045154
Nagelkerke's pseudo r^2...: 0.06681
Sample size of AE DB......: 2388
Sample size of model......: 287
Missing data %............: 87.98158
Analysis of MCP1_LN.
- processing CalcificationPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Hypertension.composite +
DiabetesStatus + Stroke_history + Peripheral.interv + LDL_final,
family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) Hypertension.compositeyes DiabetesStatusDiabetes Stroke_history Peripheral.interv
0.6107 0.6170 -0.5058 -0.3380 -0.5331
LDL_final
-0.1784
Degrees of Freedom: 356 Total (i.e. Null); 351 Residual
Null Deviance: 487
Residual Deviance: 473 AIC: 485
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + LDL_final, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.9077 -1.1891 0.8111 1.0314 1.6747
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -13.508653 535.415008 -0.025 0.9799
currentDF[, PROTEIN] -0.117467 0.133102 -0.883 0.3775
Age 0.011647 0.014663 0.794 0.4270
Gendermale 0.050964 0.261515 0.195 0.8455
Hypertension.compositeyes 0.633766 0.327723 1.934 0.0531 .
DiabetesStatusDiabetes -0.551528 0.299781 -1.840 0.0658 .
SmokerCurrentyes 0.228471 0.242198 0.943 0.3455
Med.Statin.LLDyes -0.370960 0.278127 -1.334 0.1823
Med.all.antiplateletyes 0.476326 0.480376 0.992 0.3214
GFR_MDRD -0.004611 0.006700 -0.688 0.4914
BMI 0.017891 0.029667 0.603 0.5465
CAD_history 0.054534 0.260587 0.209 0.8342
Stroke_history -0.372281 0.242884 -1.533 0.1253
Peripheral.interv -0.502538 0.290451 -1.730 0.0836 .
stenose50-70% 14.016330 535.411739 0.026 0.9791
stenose70-90% 13.744409 535.411344 0.026 0.9795
stenose90-99% 13.371687 535.411338 0.025 0.9801
stenose100% (Occlusion) 14.603558 535.412918 0.027 0.9782
LDL_final -0.215662 0.117549 -1.835 0.0666 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 487.01 on 356 degrees of freedom
Residual deviance: 463.02 on 338 degrees of freedom
AIC: 501.02
Number of Fisher Scoring iterations: 12
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_LN ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_LN
Trait/outcome.............: CalcificationPlaque
Effect size...............: -0.117467
Standard error............: 0.133102
Odds ratio (effect size)..: 0.889
Lower 95% CI..............: 0.685
Upper 95% CI..............: 1.154
Z-value...................: -0.88253
P-value...................: 0.3774901
Hosmer and Lemeshow r^2...: 0.049252
Cox and Snell r^2.........: 0.064981
Nagelkerke's pseudo r^2...: 0.087292
Sample size of AE DB......: 2388
Sample size of model......: 357
Missing data %............: 85.05025
- processing CollagenPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
stenose, family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] stenose50-70% stenose70-90% stenose90-99%
21.5615 -0.7445 -0.3016 -16.3553 -16.8403
stenose100% (Occlusion)
-1.0187
Degrees of Freedom: 355 Total (i.e. Null); 350 Residual
Null Deviance: 361.2
Residual Deviance: 331.1 AIC: 343.1
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + LDL_final, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.5050 0.0963 0.5487 0.7158 1.3987
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 2.073e+01 3.956e+03 0.005 0.996
currentDF[, PROTEIN] -7.579e-01 1.895e-01 -4.000 6.33e-05 ***
Age 8.898e-03 1.858e-02 0.479 0.632
Gendermale -1.202e-01 3.466e-01 -0.347 0.729
Hypertension.compositeyes 3.705e-01 3.935e-01 0.942 0.346
DiabetesStatusDiabetes 3.649e-01 4.067e-01 0.897 0.370
SmokerCurrentyes 3.815e-01 3.075e-01 1.241 0.215
Med.Statin.LLDyes -7.689e-02 3.434e-01 -0.224 0.823
Med.all.antiplateletyes 8.168e-01 5.622e-01 1.453 0.146
GFR_MDRD -5.648e-03 8.561e-03 -0.660 0.509
BMI -7.924e-03 3.978e-02 -0.199 0.842
CAD_history 5.255e-04 3.226e-01 0.002 0.999
Stroke_history 2.142e-01 3.126e-01 0.685 0.493
Peripheral.interv -3.928e-01 3.537e-01 -1.110 0.267
stenose50-70% -4.636e-01 4.122e+03 0.000 1.000
stenose70-90% -1.654e+01 3.956e+03 -0.004 0.997
stenose90-99% -1.714e+01 3.956e+03 -0.004 0.997
stenose100% (Occlusion) -4.924e-01 4.323e+03 0.000 1.000
LDL_final 1.341e-02 1.490e-01 0.090 0.928
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 361.22 on 355 degrees of freedom
Residual deviance: 322.97 on 337 degrees of freedom
AIC: 360.97
Number of Fisher Scoring iterations: 16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_LN ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_LN
Trait/outcome.............: CollagenPlaque
Effect size...............: -0.757933
Standard error............: 0.189472
Odds ratio (effect size)..: 0.469
Lower 95% CI..............: 0.323
Upper 95% CI..............: 0.679
Z-value...................: -4.00023
P-value...................: 6.32809e-05
Hosmer and Lemeshow r^2...: 0.105893
Cox and Snell r^2.........: 0.101875
Nagelkerke's pseudo r^2...: 0.15981
Sample size of AE DB......: 2388
Sample size of model......: 356
Missing data %............: 85.09213
- processing Fat10Perc
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Gender + Hypertension.composite + Stroke_history + LDL_final,
family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Gendermale Hypertension.compositeyes Stroke_history
-3.7831 0.6416 0.8985 1.0321 0.5088
LDL_final
0.2259
Degrees of Freedom: 356 Total (i.e. Null); 351 Residual
Null Deviance: 356.2
Residual Deviance: 319.9 AIC: 331.9
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + LDL_final, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.4238 0.2761 0.4627 0.6594 1.7797
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 6.507205 882.747666 0.007 0.99412
currentDF[, PROTEIN] 0.697802 0.177591 3.929 8.52e-05 ***
Age 0.001098 0.018968 0.058 0.95384
Gendermale 1.003262 0.317905 3.156 0.00160 **
Hypertension.compositeyes 1.044345 0.398278 2.622 0.00874 **
DiabetesStatusDiabetes -0.213865 0.380839 -0.562 0.57442
SmokerCurrentyes 0.437856 0.318979 1.373 0.16985
Med.Statin.LLDyes 0.039751 0.364668 0.109 0.91320
Med.all.antiplateletyes 0.760893 0.562010 1.354 0.17578
GFR_MDRD -0.004178 0.009046 -0.462 0.64418
BMI 0.029718 0.038285 0.776 0.43761
CAD_history -0.111178 0.340924 -0.326 0.74434
Stroke_history 0.403651 0.333402 1.211 0.22601
Peripheral.interv -0.049242 0.363705 -0.135 0.89230
stenose50-70% -13.468540 882.743867 -0.015 0.98783
stenose70-90% -11.588378 882.743592 -0.013 0.98953
stenose90-99% -12.172795 882.743575 -0.014 0.98900
stenose100% (Occlusion) -11.208317 882.744476 -0.013 0.98987
LDL_final 0.243454 0.161401 1.508 0.13146
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 356.18 on 356 degrees of freedom
Residual deviance: 307.64 on 338 degrees of freedom
AIC: 345.64
Number of Fisher Scoring iterations: 13
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_LN ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_LN
Trait/outcome.............: Fat10Perc
Effect size...............: 0.697802
Standard error............: 0.177591
Odds ratio (effect size)..: 2.009
Lower 95% CI..............: 1.419
Upper 95% CI..............: 2.846
Z-value...................: 3.92926
P-value...................: 8.520779e-05
Hosmer and Lemeshow r^2...: 0.136261
Cox and Snell r^2.........: 0.127111
Nagelkerke's pseudo r^2...: 0.201357
Sample size of AE DB......: 2388
Sample size of model......: 357
Missing data %............: 85.05025
- processing IPH
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + Gender +
DiabetesStatus, family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) Age Gendermale DiabetesStatusDiabetes
-0.96814 0.02645 0.56473 -0.49874
Degrees of Freedom: 356 Total (i.e. Null); 353 Residual
Null Deviance: 405.3
Residual Deviance: 393.4 AIC: 401.4
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + LDL_final, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.0551 -1.0934 0.6553 0.7927 1.3321
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 10.465152 882.746422 0.012 0.9905
currentDF[, PROTEIN] -0.001767 0.151910 -0.012 0.9907
Age 0.023315 0.016565 1.407 0.1593
Gendermale 0.647257 0.282325 2.293 0.0219 *
Hypertension.compositeyes 0.264155 0.361345 0.731 0.4648
DiabetesStatusDiabetes -0.513751 0.318782 -1.612 0.1070
SmokerCurrentyes 0.043114 0.272151 0.158 0.8741
Med.Statin.LLDyes -0.228883 0.321223 -0.713 0.4761
Med.all.antiplateletyes 0.604463 0.505534 1.196 0.2318
GFR_MDRD -0.003312 0.007689 -0.431 0.6667
BMI 0.020180 0.032770 0.616 0.5380
CAD_history 0.209279 0.302856 0.691 0.4896
Stroke_history 0.190935 0.278732 0.685 0.4933
Peripheral.interv 0.425060 0.347900 1.222 0.2218
stenose50-70% -12.083404 882.743894 -0.014 0.9891
stenose70-90% -12.665178 882.743525 -0.014 0.9886
stenose90-99% -12.604666 882.743521 -0.014 0.9886
stenose100% (Occlusion) -12.113730 882.744448 -0.014 0.9891
LDL_final 0.067077 0.135123 0.496 0.6196
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 405.31 on 356 degrees of freedom
Residual deviance: 386.17 on 338 degrees of freedom
AIC: 424.17
Number of Fisher Scoring iterations: 13
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_LN ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_LN
Trait/outcome.............: IPH
Effect size...............: -0.001767
Standard error............: 0.15191
Odds ratio (effect size)..: 0.998
Lower 95% CI..............: 0.741
Upper 95% CI..............: 1.344
Z-value...................: -0.011635
P-value...................: 0.9907169
Hosmer and Lemeshow r^2...: 0.047226
Cox and Snell r^2.........: 0.052204
Nagelkerke's pseudo r^2...: 0.07692
Sample size of AE DB......: 2388
Sample size of model......: 357
Missing data %............: 85.05025
Analysis of IL6_pg_ug_2015_LN.
- processing CalcificationPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + SmokerCurrent +
LDL_final, family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) Age SmokerCurrentyes LDL_final
-1.27196 0.02486 0.41455 -0.16393
Degrees of Freedom: 619 Total (i.e. Null); 616 Residual
Null Deviance: 857.9
Residual Deviance: 843 AIC: 851
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + LDL_final, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.7301 -1.1762 0.8182 1.0943 1.7609
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -1.830909 1.736196 -1.055 0.29163
currentDF[, PROTEIN] -0.028728 0.057440 -0.500 0.61698
Age 0.029085 0.010297 2.825 0.00474 **
Gendermale 0.053215 0.184708 0.288 0.77327
Hypertension.compositeyes 0.190353 0.256764 0.741 0.45848
DiabetesStatusDiabetes -0.117627 0.208990 -0.563 0.57355
SmokerCurrentyes 0.474415 0.183594 2.584 0.00976 **
Med.Statin.LLDyes -0.273636 0.211301 -1.295 0.19532
Med.all.antiplateletyes 0.133791 0.293567 0.456 0.64858
GFR_MDRD 0.005052 0.004528 1.116 0.26449
BMI 0.018513 0.022445 0.825 0.40948
CAD_history 0.017676 0.193243 0.091 0.92712
Stroke_history -0.224433 0.181531 -1.236 0.21633
Peripheral.interv -0.330490 0.219559 -1.505 0.13226
stenose50-70% -1.298081 1.303665 -0.996 0.31939
stenose70-90% -0.541387 1.245175 -0.435 0.66372
stenose90-99% -0.445735 1.243566 -0.358 0.72002
stenose100% (Occlusion) 0.165121 1.512199 0.109 0.91305
stenose70-99% -15.714990 605.326073 -0.026 0.97929
LDL_final -0.209000 0.086579 -2.414 0.01578 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 857.85 on 619 degrees of freedom
Residual deviance: 824.82 on 600 degrees of freedom
AIC: 864.82
Number of Fisher Scoring iterations: 13
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_LN ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_pg_ug_2015_LN
Trait/outcome.............: CalcificationPlaque
Effect size...............: -0.028728
Standard error............: 0.05744
Odds ratio (effect size)..: 0.972
Lower 95% CI..............: 0.868
Upper 95% CI..............: 1.087
Z-value...................: -0.500136
P-value...................: 0.6169792
Hosmer and Lemeshow r^2...: 0.038502
Cox and Snell r^2.........: 0.051879
Nagelkerke's pseudo r^2...: 0.069234
Sample size of AE DB......: 2388
Sample size of model......: 620
Missing data %............: 74.03685
- processing CollagenPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
SmokerCurrent, family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] SmokerCurrentyes
0.5975 -0.1769 0.5134
Degrees of Freedom: 621 Total (i.e. Null); 619 Residual
Null Deviance: 643
Residual Deviance: 631.3 AIC: 637.3
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + LDL_final, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.2201 0.3945 0.6217 0.7404 1.0463
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.347e+01 1.384e+03 0.010 0.9922
currentDF[, PROTEIN] -1.592e-01 7.048e-02 -2.259 0.0239 *
Age 3.108e-03 1.246e-02 0.249 0.8030
Gendermale -9.898e-02 2.255e-01 -0.439 0.6608
Hypertension.compositeyes 7.679e-02 3.053e-01 0.251 0.8014
DiabetesStatusDiabetes 2.206e-01 2.605e-01 0.847 0.3971
SmokerCurrentyes 5.213e-01 2.291e-01 2.276 0.0229 *
Med.Statin.LLDyes 1.039e-01 2.526e-01 0.411 0.6809
Med.all.antiplateletyes 6.244e-02 3.510e-01 0.178 0.8588
GFR_MDRD 5.358e-03 5.437e-03 0.986 0.3244
BMI 3.671e-02 2.916e-02 1.259 0.2080
CAD_history 2.909e-01 2.390e-01 1.217 0.2235
Stroke_history 1.574e-01 2.219e-01 0.709 0.4782
Peripheral.interv 1.187e-01 2.761e-01 0.430 0.6672
stenose50-70% -1.425e+01 1.384e+03 -0.010 0.9918
stenose70-90% -1.512e+01 1.384e+03 -0.011 0.9913
stenose90-99% -1.511e+01 1.384e+03 -0.011 0.9913
stenose100% (Occlusion) 4.083e-01 1.646e+03 0.000 0.9998
stenose70-99% 3.207e-01 2.189e+03 0.000 0.9999
LDL_final 1.251e-01 1.046e-01 1.196 0.2316
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 643.00 on 621 degrees of freedom
Residual deviance: 616.63 on 602 degrees of freedom
AIC: 656.63
Number of Fisher Scoring iterations: 15
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_LN ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_pg_ug_2015_LN
Trait/outcome.............: CollagenPlaque
Effect size...............: -0.159235
Standard error............: 0.070477
Odds ratio (effect size)..: 0.853
Lower 95% CI..............: 0.743
Upper 95% CI..............: 0.979
Z-value...................: -2.259392
P-value...................: 0.023859
Hosmer and Lemeshow r^2...: 0.041007
Cox and Snell r^2.........: 0.041506
Nagelkerke's pseudo r^2...: 0.064417
Sample size of AE DB......: 2388
Sample size of model......: 622
Missing data %............: 73.9531
- processing Fat10Perc
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Gender + Stroke_history + Peripheral.interv + LDL_final,
family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Gendermale Stroke_history Peripheral.interv LDL_final
1.0123 0.3281 0.9595 0.3484 -0.5311 0.1690
Degrees of Freedom: 621 Total (i.e. Null); 616 Residual
Null Deviance: 717.6
Residual Deviance: 658.9 AIC: 670.9
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + LDL_final, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.3891 -0.9341 0.5910 0.7839 1.4787
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 13.890440 499.839451 0.028 0.9778
currentDF[, PROTEIN] 0.328587 0.070060 4.690 2.73e-06 ***
Age 0.004246 0.011788 0.360 0.7187
Gendermale 0.984317 0.208140 4.729 2.26e-06 ***
Hypertension.compositeyes 0.166914 0.297912 0.560 0.5753
DiabetesStatusDiabetes -0.110118 0.239927 -0.459 0.6463
SmokerCurrentyes 0.145975 0.213639 0.683 0.4944
Med.Statin.LLDyes -0.280475 0.256255 -1.095 0.2737
Med.all.antiplateletyes 0.217329 0.337911 0.643 0.5201
GFR_MDRD -0.004063 0.005325 -0.763 0.4455
BMI 0.014926 0.025508 0.585 0.5585
CAD_history 0.160808 0.227127 0.708 0.4789
Stroke_history 0.350025 0.221650 1.579 0.1143
Peripheral.interv -0.583853 0.239819 -2.435 0.0149 *
stenose50-70% -13.733097 499.837635 -0.027 0.9781
stenose70-90% -13.369672 499.837472 -0.027 0.9787
stenose90-99% -13.410025 499.837467 -0.027 0.9786
stenose100% (Occlusion) -14.275461 499.838084 -0.029 0.9772
stenose70-99% -14.439735 499.839587 -0.029 0.9770
LDL_final 0.150654 0.101254 1.488 0.1368
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 717.61 on 621 degrees of freedom
Residual deviance: 650.20 on 602 degrees of freedom
AIC: 690.2
Number of Fisher Scoring iterations: 13
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_LN ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_pg_ug_2015_LN
Trait/outcome.............: Fat10Perc
Effect size...............: 0.328587
Standard error............: 0.07006
Odds ratio (effect size)..: 1.389
Lower 95% CI..............: 1.211
Upper 95% CI..............: 1.593
Z-value...................: 4.690096
P-value...................: 2.730764e-06
Hosmer and Lemeshow r^2...: 0.093938
Cox and Snell r^2.........: 0.102711
Nagelkerke's pseudo r^2...: 0.150045
Sample size of AE DB......: 2388
Sample size of model......: 622
Missing data %............: 73.9531
- processing IPH
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + Gender +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + CAD_history +
Stroke_history + Peripheral.interv, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) Age Gendermale SmokerCurrentyes Med.Statin.LLDyes
-1.75978 0.01881 0.57921 0.34166 -0.34769
Med.all.antiplateletyes CAD_history Stroke_history Peripheral.interv
0.61066 0.29144 0.30926 0.34737
Degrees of Freedom: 620 Total (i.e. Null); 612 Residual
Null Deviance: 814.3
Residual Deviance: 786.5 AIC: 804.5
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + LDL_final, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.0239 -1.2546 0.7741 0.9506 1.3869
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -2.316877 1.768092 -1.310 0.1901
currentDF[, PROTEIN] 0.052884 0.059583 0.888 0.3748
Age 0.019546 0.010575 1.848 0.0646 .
Gendermale 0.616521 0.188836 3.265 0.0011 **
Hypertension.compositeyes 0.100958 0.262302 0.385 0.7003
DiabetesStatusDiabetes -0.109138 0.214949 -0.508 0.6116
SmokerCurrentyes 0.333127 0.190690 1.747 0.0806 .
Med.Statin.LLDyes -0.276458 0.223380 -1.238 0.2159
Med.all.antiplateletyes 0.603370 0.296094 2.038 0.0416 *
GFR_MDRD -0.001990 0.004712 -0.422 0.6727
BMI 0.013982 0.023105 0.605 0.5451
CAD_history 0.291956 0.203434 1.435 0.1512
Stroke_history 0.314760 0.192264 1.637 0.1016
Peripheral.interv 0.334848 0.234754 1.426 0.1538
stenose50-70% -0.143551 1.303399 -0.110 0.9123
stenose70-90% 0.054200 1.249010 0.043 0.9654
stenose90-99% 0.162808 1.247403 0.131 0.8962
stenose100% (Occlusion) -0.001599 1.473861 -0.001 0.9991
stenose70-99% -0.827068 1.907263 -0.434 0.6645
LDL_final 0.078110 0.088317 0.884 0.3765
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 814.31 on 620 degrees of freedom
Residual deviance: 782.48 on 601 degrees of freedom
AIC: 822.48
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_LN ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_pg_ug_2015_LN
Trait/outcome.............: IPH
Effect size...............: 0.052884
Standard error............: 0.059583
Odds ratio (effect size)..: 1.054
Lower 95% CI..............: 0.938
Upper 95% CI..............: 1.185
Z-value...................: 0.88757
P-value...................: 0.3747719
Hosmer and Lemeshow r^2...: 0.039092
Cox and Snell r^2.........: 0.049969
Nagelkerke's pseudo r^2...: 0.068401
Sample size of AE DB......: 2388
Sample size of model......: 621
Missing data %............: 73.99497
Analysis of IL6R_pg_ug_2015_LN.
- processing CalcificationPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + SmokerCurrent +
Peripheral.interv + stenose + LDL_final, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) Age SmokerCurrentyes Peripheral.interv stenose50-70%
13.3099 0.0214 0.3256 -0.3539 -15.0020
stenose70-90% stenose90-99% stenose100% (Occlusion) stenose70-99% LDL_final
-14.3302 -14.2143 -13.5827 -29.4019 -0.1460
Degrees of Freedom: 613 Total (i.e. Null); 604 Residual
Null Deviance: 849.3
Residual Deviance: 826.6 AIC: 846.6
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + LDL_final, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.6719 -1.1921 0.8332 1.1105 1.6235
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.255e+01 6.239e+02 0.020 0.9840
currentDF[, PROTEIN] -1.070e-01 7.800e-02 -1.371 0.1703
Age 2.317e-02 1.049e-02 2.209 0.0272 *
Gendermale 6.011e-03 1.850e-01 0.032 0.9741
Hypertension.compositeyes 2.254e-01 2.520e-01 0.895 0.3710
DiabetesStatusDiabetes -1.754e-01 2.122e-01 -0.826 0.4086
SmokerCurrentyes 3.559e-01 1.834e-01 1.940 0.0524 .
Med.Statin.LLDyes -2.060e-01 2.141e-01 -0.962 0.3359
Med.all.antiplateletyes 1.227e-01 2.978e-01 0.412 0.6802
GFR_MDRD 3.300e-03 4.655e-03 0.709 0.4784
BMI 8.552e-03 2.310e-02 0.370 0.7112
CAD_history 8.926e-04 1.935e-01 0.005 0.9963
Stroke_history -1.823e-01 1.817e-01 -1.003 0.3158
Peripheral.interv -3.495e-01 2.219e-01 -1.575 0.1153
stenose50-70% -1.507e+01 6.239e+02 -0.024 0.9807
stenose70-90% -1.432e+01 6.239e+02 -0.023 0.9817
stenose90-99% -1.421e+01 6.239e+02 -0.023 0.9818
stenose100% (Occlusion) -1.366e+01 6.239e+02 -0.022 0.9825
stenose70-99% -2.955e+01 8.717e+02 -0.034 0.9730
LDL_final -1.777e-01 8.767e-02 -2.027 0.0426 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 849.30 on 613 degrees of freedom
Residual deviance: 820.09 on 594 degrees of freedom
AIC: 860.09
Number of Fisher Scoring iterations: 13
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_LN ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6R_pg_ug_2015_LN
Trait/outcome.............: CalcificationPlaque
Effect size...............: -0.106974
Standard error............: 0.078004
Odds ratio (effect size)..: 0.899
Lower 95% CI..............: 0.771
Upper 95% CI..............: 1.047
Z-value...................: -1.371396
P-value...................: 0.1702514
Hosmer and Lemeshow r^2...: 0.034389
Cox and Snell r^2.........: 0.046454
Nagelkerke's pseudo r^2...: 0.062002
Sample size of AE DB......: 2388
Sample size of model......: 614
Missing data %............: 74.28811
- processing CollagenPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ SmokerCurrent,
family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) SmokerCurrentyes
1.1229 0.4918
Degrees of Freedom: 615 Total (i.e. Null); 614 Residual
Null Deviance: 642.7
Residual Deviance: 637.2 AIC: 641.2
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + LDL_final, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.1867 0.4317 0.6442 0.7516 1.0235
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.362e+01 1.696e+03 0.008 0.9936
currentDF[, PROTEIN] 4.167e-03 9.006e-02 0.046 0.9631
Age 5.593e-03 1.253e-02 0.446 0.6554
Gendermale -3.436e-03 2.219e-01 -0.015 0.9876
Hypertension.compositeyes 2.294e-03 3.016e-01 0.008 0.9939
DiabetesStatusDiabetes 2.969e-01 2.630e-01 1.129 0.2589
SmokerCurrentyes 5.129e-01 2.286e-01 2.244 0.0249 *
Med.Statin.LLDyes 1.524e-01 2.529e-01 0.603 0.5468
Med.all.antiplateletyes 2.126e-01 3.455e-01 0.615 0.5383
GFR_MDRD 3.779e-03 5.528e-03 0.684 0.4942
BMI 3.788e-02 2.895e-02 1.308 0.1908
CAD_history 2.453e-01 2.369e-01 1.036 0.3003
Stroke_history 8.335e-02 2.201e-01 0.379 0.7050
Peripheral.interv 1.403e-01 2.762e-01 0.508 0.6116
stenose50-70% -1.412e+01 1.696e+03 -0.008 0.9934
stenose70-90% -1.507e+01 1.696e+03 -0.009 0.9929
stenose90-99% -1.496e+01 1.696e+03 -0.009 0.9930
stenose100% (Occlusion) 5.649e-01 1.918e+03 0.000 0.9998
stenose70-99% 6.286e-01 2.399e+03 0.000 0.9998
LDL_final 1.174e-01 1.055e-01 1.112 0.2660
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 642.71 on 615 degrees of freedom
Residual deviance: 622.54 on 596 degrees of freedom
AIC: 662.54
Number of Fisher Scoring iterations: 15
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_LN ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6R_pg_ug_2015_LN
Trait/outcome.............: CollagenPlaque
Effect size...............: 0.004167
Standard error............: 0.090058
Odds ratio (effect size)..: 1.004
Lower 95% CI..............: 0.842
Upper 95% CI..............: 1.198
Z-value...................: 0.046265
P-value...................: 0.9630989
Hosmer and Lemeshow r^2...: 0.031378
Cox and Snell r^2.........: 0.032208
Nagelkerke's pseudo r^2...: 0.049725
Sample size of AE DB......: 2388
Sample size of model......: 616
Missing data %............: 74.20436
- processing Fat10Perc
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Gender + Stroke_history +
Peripheral.interv + LDL_final, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) Gendermale Stroke_history Peripheral.interv LDL_final
-0.1296 0.9069 0.4893 -0.5359 0.1945
Degrees of Freedom: 615 Total (i.e. Null); 611 Residual
Null Deviance: 705.7
Residual Deviance: 669.9 AIC: 679.9
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + LDL_final, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.2582 -1.0691 0.6233 0.7802 1.3523
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 13.369868 623.666426 0.021 0.9829
currentDF[, PROTEIN] 0.054158 0.085467 0.634 0.5263
Age 0.002498 0.012054 0.207 0.8359
Gendermale 0.948277 0.205225 4.621 3.83e-06 ***
Hypertension.compositeyes 0.185882 0.287940 0.646 0.5186
DiabetesStatusDiabetes -0.045731 0.241994 -0.189 0.8501
SmokerCurrentyes 0.202464 0.211826 0.956 0.3392
Med.Statin.LLDyes -0.255734 0.259282 -0.986 0.3240
Med.all.antiplateletyes 0.193744 0.342976 0.565 0.5721
GFR_MDRD -0.002854 0.005456 -0.523 0.6008
BMI -0.008758 0.026188 -0.334 0.7381
CAD_history 0.047658 0.223185 0.214 0.8309
Stroke_history 0.470947 0.220512 2.136 0.0327 *
Peripheral.interv -0.594754 0.239320 -2.485 0.0129 *
stenose50-70% -13.706507 623.665074 -0.022 0.9825
stenose70-90% -13.169212 623.664944 -0.021 0.9832
stenose90-99% -13.284749 623.664938 -0.021 0.9830
stenose100% (Occlusion) -14.088904 623.665442 -0.023 0.9820
stenose70-99% -14.595709 623.666628 -0.023 0.9813
LDL_final 0.162951 0.102327 1.592 0.1113
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 705.67 on 615 degrees of freedom
Residual deviance: 662.08 on 596 degrees of freedom
AIC: 702.08
Number of Fisher Scoring iterations: 13
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_LN ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6R_pg_ug_2015_LN
Trait/outcome.............: Fat10Perc
Effect size...............: 0.054158
Standard error............: 0.085467
Odds ratio (effect size)..: 1.056
Lower 95% CI..............: 0.893
Upper 95% CI..............: 1.248
Z-value...................: 0.633675
P-value...................: 0.526293
Hosmer and Lemeshow r^2...: 0.06178
Cox and Snell r^2.........: 0.068327
Nagelkerke's pseudo r^2...: 0.100192
Sample size of AE DB......: 2388
Sample size of model......: 616
Missing data %............: 74.20436
- processing IPH
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + SmokerCurrent + Med.all.antiplatelet + Peripheral.interv,
family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Age Gendermale SmokerCurrentyes
-1.73188 0.14752 0.02187 0.62959 0.30390
Med.all.antiplateletyes Peripheral.interv
0.51845 0.32579
Degrees of Freedom: 614 Total (i.e. Null); 608 Residual
Null Deviance: 809.9
Residual Deviance: 786.7 AIC: 800.7
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + LDL_final, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.0032 -1.2447 0.7716 0.9589 1.3876
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -1.9710605 1.8997910 -1.038 0.299496
currentDF[, PROTEIN] 0.1228828 0.0774451 1.587 0.112579
Age 0.0166300 0.0108084 1.539 0.123897
Gendermale 0.6555481 0.1889490 3.469 0.000522 ***
Hypertension.compositeyes 0.1050767 0.2577038 0.408 0.683463
DiabetesStatusDiabetes -0.1861702 0.2167506 -0.859 0.390388
SmokerCurrentyes 0.2894346 0.1915645 1.511 0.130814
Med.Statin.LLDyes -0.2219860 0.2264142 -0.980 0.326868
Med.all.antiplateletyes 0.5140017 0.3042486 1.689 0.091140 .
GFR_MDRD -0.0036757 0.0048703 -0.755 0.450417
BMI -0.0002495 0.0236391 -0.011 0.991580
CAD_history 0.2679931 0.2036027 1.316 0.188089
Stroke_history 0.2994626 0.1924626 1.556 0.119719
Peripheral.interv 0.3405722 0.2386829 1.427 0.153614
stenose50-70% 0.4040515 1.4880029 0.272 0.785976
stenose70-90% 0.4991339 1.4377983 0.347 0.728477
stenose90-99% 0.5493762 1.4363202 0.382 0.702099
stenose100% (Occlusion) 0.4420048 1.6386209 0.270 0.787359
stenose70-99% -0.3107490 2.0346938 -0.153 0.878615
LDL_final 0.0850495 0.0902429 0.942 0.345962
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 809.93 on 614 degrees of freedom
Residual deviance: 778.33 on 595 degrees of freedom
AIC: 818.33
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_LN ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6R_pg_ug_2015_LN
Trait/outcome.............: IPH
Effect size...............: 0.122883
Standard error............: 0.077445
Odds ratio (effect size)..: 1.131
Lower 95% CI..............: 0.972
Upper 95% CI..............: 1.316
Z-value...................: 1.586708
P-value...................: 0.1125789
Hosmer and Lemeshow r^2...: 0.039017
Cox and Snell r^2.........: 0.050086
Nagelkerke's pseudo r^2...: 0.068418
Sample size of AE DB......: 2388
Sample size of model......: 615
Missing data %............: 74.24623
Analysis of MCP1_pg_ug_2015_LN.
- processing CalcificationPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + SmokerCurrent + Peripheral.interv + LDL_final, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Age SmokerCurrentyes Peripheral.interv LDL_final
-1.73495 -0.31223 0.02487 0.40727 -0.31242 -0.11567
Degrees of Freedom: 635 Total (i.e. Null); 630 Residual
Null Deviance: 880.3
Residual Deviance: 839.5 AIC: 851.5
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + LDL_final, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.8203 -1.1443 0.7133 1.0820 1.7743
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -2.186778 1.751653 -1.248 0.21188
currentDF[, PROTEIN] -0.313354 0.066094 -4.741 2.13e-06 ***
Age 0.028386 0.010421 2.724 0.00645 **
Gendermale 0.077519 0.185548 0.418 0.67610
Hypertension.compositeyes 0.162429 0.253659 0.640 0.52195
DiabetesStatusDiabetes -0.117603 0.210834 -0.558 0.57698
SmokerCurrentyes 0.437906 0.184123 2.378 0.01739 *
Med.Statin.LLDyes -0.263210 0.212928 -1.236 0.21640
Med.all.antiplateletyes 0.171668 0.291815 0.588 0.55635
GFR_MDRD 0.003704 0.004551 0.814 0.41574
BMI 0.012750 0.022517 0.566 0.57124
CAD_history 0.032059 0.193000 0.166 0.86807
Stroke_history -0.126189 0.182798 -0.690 0.48999
Peripheral.interv -0.359200 0.221288 -1.623 0.10454
stenose50-70% -1.264011 1.313877 -0.962 0.33603
stenose70-90% -0.354338 1.252555 -0.283 0.77726
stenose90-99% -0.337422 1.251122 -0.270 0.78739
stenose100% (Occlusion) 0.116115 1.519853 0.076 0.93910
stenose70-99% -15.425510 596.420676 -0.026 0.97937
LDL_final -0.150771 0.087187 -1.729 0.08376 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 880.27 on 635 degrees of freedom
Residual deviance: 826.00 on 616 degrees of freedom
AIC: 866
Number of Fisher Scoring iterations: 13
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_LN ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_LN
Trait/outcome.............: CalcificationPlaque
Effect size...............: -0.313354
Standard error............: 0.066094
Odds ratio (effect size)..: 0.731
Lower 95% CI..............: 0.642
Upper 95% CI..............: 0.832
Z-value...................: -4.741015
P-value...................: 2.126505e-06
Hosmer and Lemeshow r^2...: 0.061647
Cox and Snell r^2.........: 0.081785
Nagelkerke's pseudo r^2...: 0.109128
Sample size of AE DB......: 2388
Sample size of model......: 636
Missing data %............: 73.36683
- processing CollagenPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
SmokerCurrent, family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] SmokerCurrentyes
0.8981 -0.2238 0.5088
Degrees of Freedom: 637 Total (i.e. Null); 635 Residual
Null Deviance: 658.5
Residual Deviance: 643.5 AIC: 649.5
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + LDL_final, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.2910 0.3778 0.6175 0.7387 1.1095
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.342e+01 1.382e+03 0.010 0.99225
currentDF[, PROTEIN] -2.208e-01 7.866e-02 -2.807 0.00501 **
Age 4.608e-03 1.243e-02 0.371 0.71083
Gendermale -1.078e-02 2.224e-01 -0.048 0.96133
Hypertension.compositeyes -5.278e-02 3.030e-01 -0.174 0.86171
DiabetesStatusDiabetes 2.626e-01 2.605e-01 1.008 0.31344
SmokerCurrentyes 5.090e-01 2.287e-01 2.226 0.02604 *
Med.Statin.LLDyes 1.414e-01 2.510e-01 0.563 0.57335
Med.all.antiplateletyes 8.044e-02 3.422e-01 0.235 0.81417
GFR_MDRD 4.835e-03 5.415e-03 0.893 0.37194
BMI 3.947e-02 2.890e-02 1.366 0.17194
CAD_history 2.561e-01 2.351e-01 1.089 0.27610
Stroke_history 1.127e-01 2.197e-01 0.513 0.60798
Peripheral.interv 1.576e-01 2.759e-01 0.571 0.56785
stenose50-70% -1.419e+01 1.382e+03 -0.010 0.99181
stenose70-90% -1.505e+01 1.382e+03 -0.011 0.99131
stenose90-99% -1.500e+01 1.382e+03 -0.011 0.99134
stenose100% (Occlusion) 2.831e-01 1.649e+03 0.000 0.99986
stenose70-99% 7.185e-01 2.188e+03 0.000 0.99974
LDL_final 1.570e-01 1.054e-01 1.489 0.13646
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 658.5 on 637 degrees of freedom
Residual deviance: 628.5 on 618 degrees of freedom
AIC: 668.5
Number of Fisher Scoring iterations: 15
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_LN ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_LN
Trait/outcome.............: CollagenPlaque
Effect size...............: -0.220764
Standard error............: 0.078656
Odds ratio (effect size)..: 0.802
Lower 95% CI..............: 0.687
Upper 95% CI..............: 0.936
Z-value...................: -2.806693
P-value...................: 0.005005292
Hosmer and Lemeshow r^2...: 0.045568
Cox and Snell r^2.........: 0.045943
Nagelkerke's pseudo r^2...: 0.071367
Sample size of AE DB......: 2388
Sample size of model......: 638
Missing data %............: 73.28308
- processing Fat10Perc
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Gender + Stroke_history + Peripheral.interv + LDL_final,
family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Gendermale Stroke_history Peripheral.interv LDL_final
0.08208 0.10797 0.94650 0.42457 -0.48857 0.15348
Degrees of Freedom: 637 Total (i.e. Null); 632 Residual
Null Deviance: 737.7
Residual Deviance: 697.9 AIC: 709.9
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + LDL_final, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.2927 -1.0871 0.6267 0.7942 1.3858
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 13.502340 501.501259 0.027 0.9785
currentDF[, PROTEIN] 0.106038 0.070527 1.504 0.1327
Age 0.001382 0.011569 0.119 0.9049
Gendermale 0.995947 0.200571 4.966 6.85e-07 ***
Hypertension.compositeyes 0.211642 0.284536 0.744 0.4570
DiabetesStatusDiabetes -0.110556 0.233413 -0.474 0.6357
SmokerCurrentyes 0.232019 0.207172 1.120 0.2627
Med.Statin.LLDyes -0.285167 0.252105 -1.131 0.2580
Med.all.antiplateletyes 0.282374 0.325554 0.867 0.3857
GFR_MDRD -0.005622 0.005182 -1.085 0.2780
BMI 0.001346 0.024744 0.054 0.9566
CAD_history 0.050724 0.217427 0.233 0.8155
Stroke_history 0.398954 0.216374 1.844 0.0652 .
Peripheral.interv -0.546898 0.234383 -2.333 0.0196 *
stenose50-70% -13.715798 501.499552 -0.027 0.9782
stenose70-90% -13.286922 501.499395 -0.026 0.9789
stenose90-99% -13.413649 501.499389 -0.027 0.9787
stenose100% (Occlusion) -14.108936 501.499989 -0.028 0.9776
stenose70-99% -14.820496 501.501493 -0.030 0.9764
LDL_final 0.128934 0.098440 1.310 0.1903
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 737.67 on 637 degrees of freedom
Residual deviance: 688.27 on 618 degrees of freedom
AIC: 728.27
Number of Fisher Scoring iterations: 13
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_LN ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_LN
Trait/outcome.............: Fat10Perc
Effect size...............: 0.106038
Standard error............: 0.070527
Odds ratio (effect size)..: 1.112
Lower 95% CI..............: 0.968
Upper 95% CI..............: 1.277
Z-value...................: 1.503511
P-value...................: 0.1327074
Hosmer and Lemeshow r^2...: 0.066965
Cox and Snell r^2.........: 0.074504
Nagelkerke's pseudo r^2...: 0.108713
Sample size of AE DB......: 2388
Sample size of model......: 638
Missing data %............: 73.28308
- processing IPH
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + Gender +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + CAD_history +
Stroke_history + Peripheral.interv, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) Age Gendermale SmokerCurrentyes Med.Statin.LLDyes
-1.73057 0.01821 0.60621 0.34901 -0.40268
Med.all.antiplateletyes CAD_history Stroke_history Peripheral.interv
0.60993 0.30075 0.34840 0.39779
Degrees of Freedom: 636 Total (i.e. Null); 628 Residual
Null Deviance: 837.7
Residual Deviance: 806.3 AIC: 824.3
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + LDL_final, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.9694 -1.2529 0.7654 0.9505 1.4993
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -2.236604 1.772434 -1.262 0.206991
currentDF[, PROTEIN] -0.088207 0.064866 -1.360 0.173883
Age 0.017937 0.010555 1.699 0.089230 .
Gendermale 0.654559 0.186699 3.506 0.000455 ***
Hypertension.compositeyes 0.042545 0.257261 0.165 0.868647
DiabetesStatusDiabetes -0.151501 0.212570 -0.713 0.476025
SmokerCurrentyes 0.337455 0.188614 1.789 0.073594 .
Med.Statin.LLDyes -0.371866 0.223949 -1.660 0.096815 .
Med.all.antiplateletyes 0.590621 0.291944 2.023 0.043066 *
GFR_MDRD -0.002562 0.004687 -0.547 0.584607
BMI 0.007867 0.022713 0.346 0.729077
CAD_history 0.301254 0.199352 1.511 0.130746
Stroke_history 0.389073 0.191490 2.032 0.042172 *
Peripheral.interv 0.397429 0.234324 1.696 0.089873 .
stenose50-70% -0.120217 1.318577 -0.091 0.927356
stenose70-90% 0.122338 1.264558 0.097 0.922930
stenose90-99% 0.191060 1.263050 0.151 0.879764
stenose100% (Occlusion) -0.036943 1.489262 -0.025 0.980210
stenose70-99% -0.906126 1.915305 -0.473 0.636144
LDL_final 0.071514 0.088157 0.811 0.417243
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 837.69 on 636 degrees of freedom
Residual deviance: 801.66 on 617 degrees of freedom
AIC: 841.66
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_LN ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_LN
Trait/outcome.............: IPH
Effect size...............: -0.088207
Standard error............: 0.064866
Odds ratio (effect size)..: 0.916
Lower 95% CI..............: 0.806
Upper 95% CI..............: 1.04
Z-value...................: -1.359831
P-value...................: 0.1738834
Hosmer and Lemeshow r^2...: 0.043019
Cox and Snell r^2.........: 0.055001
Nagelkerke's pseudo r^2...: 0.075186
Sample size of AE DB......: 2388
Sample size of model......: 637
Missing data %............: 73.32496 cat("Edit the column names...\n")Edit the column names...colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
"Beta", "s.e.m.",
"OR", "low95CI", "up95CI",
"Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")
cat("Correct the variable types...\n")Correct the variable types...GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)
# Save the data
cat("Writing results to Excel-file...\n")Writing results to Excel-file...### Univariate
write.xlsx(GLM.results,
file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Multi.Protein.PlaquePhenotypes.MODEL3.xlsx"),
row.names = FALSE, col.names = TRUE, sheetName = "Bin.Multi.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")Removing intermediate files...rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)
Inverse-rank normalization
Here we use the inverse-rank normalized data - visually this is more normally distributed.
Analysis of continuous/quantitative plaque traits as a function of serum/plaque IL6(R)/MCP1 levels.
GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")Running linear regression...for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
PROTEIN = TRAITS.PROTEIN.RANK[protein]
cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
for (trait in 1:length(TRAITS.CON.RANK)) {
TRAIT = TRAITS.CON.RANK[trait]
cat(paste0("\n- processing ",TRAIT,"\n\n"))
currentDF <- as.data.frame(AEDB.CEA %>%
dplyr::select(., PROTEIN, TRAIT, COVARIATES_M3) %>%
filter(complete.cases(.))) %>%
filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
# for debug
# print(DT::datatable(currentDF))
# print(nrow(currentDF))
# print(str(currentDF))
### univariate
fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender +
Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose + LDL_final,
data = currentDF)
model_step <- stepAIC(fit, direction = "both", trace = FALSE)
print(model_step)
print(summary(fit))
GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
GLM.results.TEMP[1,] = GLM.CON(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
GLM.results = rbind(GLM.results, GLM.results.TEMP)
}
}
Analysis of IL6_rank.
- processing Macrophages_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ SmokerCurrent + Med.Statin.LLD,
data = currentDF)
Coefficients:
(Intercept) SmokerCurrentyes Med.Statin.LLDyes
0.03459 0.16805 -0.16826
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose +
LDL_final, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1.92124 -0.73316 -0.00557 0.71379 3.03756
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.792704 1.329636 2.852 0.00462 **
currentDF[, TRAIT] -0.061504 0.050080 -1.228 0.22030
Age -0.009624 0.006942 -1.386 0.16658
Gendermale -0.159404 0.125754 -1.268 0.20587
Hypertension.compositeyes 0.104571 0.162654 0.643 0.52074
DiabetesStatusDiabetes -0.132718 0.145494 -0.912 0.36235
SmokerCurrentyes 0.109285 0.116428 0.939 0.34862
Med.Statin.LLDyes -0.278661 0.131209 -2.124 0.03445 *
Med.all.antiplateletyes -0.227481 0.234994 -0.968 0.33376
GFR_MDRD 0.001924 0.003229 0.596 0.55182
BMI -0.017054 0.014966 -1.140 0.25533
CAD_history 0.243533 0.123997 1.964 0.05039 .
Stroke_history 0.102115 0.117514 0.869 0.38552
Peripheral.interv -0.116734 0.137714 -0.848 0.39726
stenose50-70% -2.456211 1.045001 -2.350 0.01936 *
stenose70-90% -2.333147 0.999077 -2.335 0.02014 *
stenose90-99% -2.375923 0.998637 -2.379 0.01794 *
stenose100% (Occlusion) -2.851865 1.098896 -2.595 0.00989 **
LDL_final -0.054180 0.056915 -0.952 0.34184
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9784 on 321 degrees of freedom
Multiple R-squared: 0.06494, Adjusted R-squared: 0.01251
F-statistic: 1.239 on 18 and 321 DF, p-value: 0.2282
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_rank ' with ' Macrophages_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_rank
Trait/outcome.............: Macrophages_rank
Effect size...............: -0.061504
Standard error............: 0.05008
Odds ratio (effect size)..: 0.94
Lower 95% CI..............: 0.852
Upper 95% CI..............: 1.037
T-value...................: -1.228115
P-value...................: 0.2203034
R^2.......................: 0.064943
Adjusted r^2..............: 0.01251
Sample size of AE DB......: 2388
Sample size of model......: 340
Missing data %............: 85.76214
- processing SMC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Med.Statin.LLD, data = currentDF)
Coefficients:
(Intercept) Med.Statin.LLDyes
0.1105 -0.1671
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose +
LDL_final, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1.94593 -0.72114 -0.00767 0.72157 2.95142
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.783029 1.343497 2.816 0.00517 **
currentDF[, TRAIT] 0.040659 0.053776 0.756 0.45016
Age -0.009438 0.007120 -1.326 0.18592
Gendermale -0.147744 0.128474 -1.150 0.25101
Hypertension.compositeyes 0.064319 0.163151 0.394 0.69368
DiabetesStatusDiabetes -0.127047 0.146769 -0.866 0.38735
SmokerCurrentyes 0.097880 0.117339 0.834 0.40481
Med.Statin.LLDyes -0.257756 0.133795 -1.927 0.05493 .
Med.all.antiplateletyes -0.197395 0.237058 -0.833 0.40564
GFR_MDRD 0.001715 0.003269 0.525 0.60016
BMI -0.019334 0.015123 -1.278 0.20201
CAD_history 0.196926 0.125287 1.572 0.11699
Stroke_history 0.081858 0.118994 0.688 0.49201
Peripheral.interv -0.090695 0.139342 -0.651 0.51559
stenose50-70% -2.414960 1.057550 -2.284 0.02306 *
stenose70-90% -2.286060 1.010233 -2.263 0.02431 *
stenose90-99% -2.289655 1.008776 -2.270 0.02389 *
stenose100% (Occlusion) -2.758852 1.109643 -2.486 0.01342 *
LDL_final -0.053128 0.058295 -0.911 0.36279
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9889 on 319 degrees of freedom
Multiple R-squared: 0.05635, Adjusted R-squared: 0.003105
F-statistic: 1.058 on 18 and 319 DF, p-value: 0.3942
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_rank ' with ' SMC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_rank
Trait/outcome.............: SMC_rank
Effect size...............: 0.040659
Standard error............: 0.053776
Odds ratio (effect size)..: 1.041
Lower 95% CI..............: 0.937
Upper 95% CI..............: 1.157
T-value...................: 0.756087
P-value...................: 0.4501553
R^2.......................: 0.056351
Adjusted r^2..............: 0.003105
Sample size of AE DB......: 2388
Sample size of model......: 338
Missing data %............: 85.8459
- processing VesselDensity_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Age + Med.Statin.LLD, data = currentDF)
Coefficients:
(Intercept) Age Med.Statin.LLDyes
0.753665 -0.009766 -0.170287
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose +
LDL_final, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1.81606 -0.73816 0.00031 0.73756 3.08388
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.913497 1.364851 2.867 0.00442 **
currentDF[, TRAIT] -0.073530 0.068216 -1.078 0.28191
Age -0.011121 0.007153 -1.555 0.12101
Gendermale -0.131235 0.128403 -1.022 0.30754
Hypertension.compositeyes 0.102116 0.166801 0.612 0.54085
DiabetesStatusDiabetes -0.124773 0.151385 -0.824 0.41045
SmokerCurrentyes 0.108612 0.119294 0.910 0.36328
Med.Statin.LLDyes -0.249016 0.134561 -1.851 0.06517 .
Med.all.antiplateletyes -0.209457 0.240938 -0.869 0.38533
GFR_MDRD 0.001910 0.003328 0.574 0.56652
BMI -0.019283 0.015234 -1.266 0.20652
CAD_history 0.201687 0.126238 1.598 0.11113
Stroke_history 0.106492 0.119862 0.888 0.37498
Peripheral.interv -0.092475 0.140901 -0.656 0.51211
stenose50-70% -2.466777 1.062496 -2.322 0.02089 *
stenose70-90% -2.346616 1.015922 -2.310 0.02155 *
stenose90-99% -2.368477 1.014795 -2.334 0.02023 *
stenose100% (Occlusion) -2.720515 1.144280 -2.377 0.01803 *
LDL_final -0.055201 0.058274 -0.947 0.34423
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.991 on 313 degrees of freedom
Multiple R-squared: 0.05833, Adjusted R-squared: 0.004181
F-statistic: 1.077 on 18 and 313 DF, p-value: 0.3742
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_rank ' with ' VesselDensity_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_rank
Trait/outcome.............: VesselDensity_rank
Effect size...............: -0.07353
Standard error............: 0.068216
Odds ratio (effect size)..: 0.929
Lower 95% CI..............: 0.813
Upper 95% CI..............: 1.062
T-value...................: -1.077905
P-value...................: 0.281906
R^2.......................: 0.058334
Adjusted r^2..............: 0.004181
Sample size of AE DB......: 2388
Sample size of model......: 332
Missing data %............: 86.09715
Analysis of MCP1_rank.
- processing Macrophages_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
Hypertension.composite + Med.all.antiplatelet + LDL_final,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale Hypertension.compositeyes Med.all.antiplateletyes
-0.7615 0.1160 0.4242 -0.2413 0.4022
LDL_final
0.1261
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose +
LDL_final, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.0118 -0.6421 0.0530 0.6167 2.7322
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.550180 1.331823 1.164 0.24525
currentDF[, TRAIT] 0.108718 0.049706 2.187 0.02940 *
Age -0.010664 0.006893 -1.547 0.12277
Gendermale 0.399090 0.123790 3.224 0.00139 **
Hypertension.compositeyes -0.256160 0.158543 -1.616 0.10708
DiabetesStatusDiabetes -0.120459 0.143222 -0.841 0.40090
SmokerCurrentyes -0.026308 0.115098 -0.229 0.81934
Med.Statin.LLDyes -0.160799 0.130582 -1.231 0.21902
Med.all.antiplateletyes 0.304046 0.223821 1.358 0.17522
GFR_MDRD -0.002907 0.003184 -0.913 0.36182
BMI -0.007932 0.014201 -0.559 0.57683
CAD_history 0.194780 0.124501 1.564 0.11863
Stroke_history 0.141896 0.116406 1.219 0.22369
Peripheral.interv -0.139426 0.141656 -0.984 0.32568
stenose50-70% -0.817147 1.069218 -0.764 0.44525
stenose70-90% -0.926314 1.024033 -0.905 0.36633
stenose90-99% -0.919539 1.023909 -0.898 0.36978
stenose100% (Occlusion) -1.623694 1.150886 -1.411 0.15921
LDL_final 0.100281 0.056049 1.789 0.07447 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.004 on 342 degrees of freedom
Multiple R-squared: 0.103, Adjusted R-squared: 0.05578
F-statistic: 2.182 on 18 and 342 DF, p-value: 0.003837
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' Macrophages_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: Macrophages_rank
Effect size...............: 0.108718
Standard error............: 0.049706
Odds ratio (effect size)..: 1.115
Lower 95% CI..............: 1.011
Upper 95% CI..............: 1.229
T-value...................: 2.187222
P-value...................: 0.0294031
R^2.......................: 0.102995
Adjusted r^2..............: 0.055784
Sample size of AE DB......: 2388
Sample size of model......: 361
Missing data %............: 84.88275
- processing SMC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + Med.all.antiplatelet +
CAD_history + Stroke_history + LDL_final, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age Gendermale Hypertension.compositeyes
-0.16113 -0.17912 -0.01124 0.38951 -0.21541
Med.all.antiplateletyes CAD_history Stroke_history LDL_final
0.34350 0.22827 0.16045 0.17107
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose +
LDL_final, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.77475 -0.66379 0.03745 0.63641 2.44650
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.761592 1.309017 1.346 0.17928
currentDF[, TRAIT] -0.175444 0.051692 -3.394 0.00077 ***
Age -0.015241 0.006873 -2.218 0.02725 *
Gendermale 0.367995 0.122914 2.994 0.00296 **
Hypertension.compositeyes -0.223901 0.154761 -1.447 0.14889
DiabetesStatusDiabetes -0.109872 0.141026 -0.779 0.43647
SmokerCurrentyes -0.037317 0.113021 -0.330 0.74147
Med.Statin.LLDyes -0.120464 0.129434 -0.931 0.35267
Med.all.antiplateletyes 0.274163 0.219821 1.247 0.21318
GFR_MDRD -0.003006 0.003132 -0.960 0.33782
BMI -0.005209 0.013963 -0.373 0.70932
CAD_history 0.254655 0.122268 2.083 0.03802 *
Stroke_history 0.154659 0.114573 1.350 0.17795
Peripheral.interv -0.150381 0.139883 -1.075 0.28312
stenose50-70% -0.846437 1.051986 -0.805 0.42161
stenose70-90% -0.983213 1.007553 -0.976 0.32984
stenose90-99% -1.000154 1.006474 -0.994 0.32107
stenose100% (Occlusion) -1.745817 1.130964 -1.544 0.12360
LDL_final 0.142069 0.055893 2.542 0.01147 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9871 on 340 degrees of freedom
Multiple R-squared: 0.1247, Adjusted R-squared: 0.07836
F-statistic: 2.691 on 18 and 340 DF, p-value: 0.0002653
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' SMC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: SMC_rank
Effect size...............: -0.175444
Standard error............: 0.051692
Odds ratio (effect size)..: 0.839
Lower 95% CI..............: 0.758
Upper 95% CI..............: 0.929
T-value...................: -3.393995
P-value...................: 0.0007701751
R^2.......................: 0.124699
Adjusted r^2..............: 0.07836
Sample size of AE DB......: 2388
Sample size of model......: 359
Missing data %............: 84.9665
- processing VesselDensity_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Gender + Med.all.antiplatelet +
LDL_final, data = currentDF)
Coefficients:
(Intercept) Gendermale Med.all.antiplateletyes LDL_final
-0.9367 0.4562 0.3756 0.1249
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose +
LDL_final, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.06281 -0.66297 0.04826 0.64889 2.70481
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.652265 1.356456 1.218 0.224053
currentDF[, TRAIT] -0.082322 0.065687 -1.253 0.210989
Age -0.010687 0.007027 -1.521 0.129238
Gendermale 0.421397 0.126183 3.340 0.000934 ***
Hypertension.compositeyes -0.198725 0.162370 -1.224 0.221848
DiabetesStatusDiabetes -0.080616 0.147908 -0.545 0.586088
SmokerCurrentyes -0.023542 0.117495 -0.200 0.841314
Med.Statin.LLDyes -0.183933 0.133849 -1.374 0.170302
Med.all.antiplateletyes 0.264553 0.225729 1.172 0.242032
GFR_MDRD -0.001982 0.003272 -0.606 0.544998
BMI -0.006037 0.014481 -0.417 0.677042
CAD_history 0.220242 0.126669 1.739 0.083004 .
Stroke_history 0.140083 0.118301 1.184 0.237203
Peripheral.interv -0.116106 0.144786 -0.802 0.423171
stenose50-70% -1.032080 1.081916 -0.954 0.340803
stenose70-90% -1.074798 1.036998 -1.036 0.300739
stenose90-99% -1.095509 1.035980 -1.057 0.291063
stenose100% (Occlusion) -1.850483 1.164954 -1.588 0.113126
LDL_final 0.085880 0.057439 1.495 0.135820
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.013 on 335 degrees of freedom
Multiple R-squared: 0.08945, Adjusted R-squared: 0.04053
F-statistic: 1.828 on 18 and 335 DF, p-value: 0.0212
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' VesselDensity_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: VesselDensity_rank
Effect size...............: -0.082322
Standard error............: 0.065687
Odds ratio (effect size)..: 0.921
Lower 95% CI..............: 0.81
Upper 95% CI..............: 1.048
T-value...................: -1.253249
P-value...................: 0.2109892
R^2.......................: 0.089453
Adjusted r^2..............: 0.040529
Sample size of AE DB......: 2388
Sample size of model......: 354
Missing data %............: 85.17588
Analysis of IL6_pg_ug_2015_rank.
- processing Macrophages_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + SmokerCurrent +
BMI + Stroke_history, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] SmokerCurrentyes BMI Stroke_history
0.43172 0.08968 0.12696 -0.01892 0.20719
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose +
LDL_final, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.3185 -0.6385 0.0175 0.6268 2.8088
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.956669 0.814353 1.175 0.2406
currentDF[, TRAIT] 0.087966 0.040062 2.196 0.0285 *
Age -0.002904 0.004882 -0.595 0.5522
Gendermale -0.009734 0.088746 -0.110 0.9127
Hypertension.compositeyes -0.054264 0.122717 -0.442 0.6585
DiabetesStatusDiabetes -0.076409 0.100157 -0.763 0.4458
SmokerCurrentyes 0.125108 0.086655 1.444 0.1493
Med.Statin.LLDyes -0.111106 0.100331 -1.107 0.2686
Med.all.antiplateletyes 0.012174 0.141146 0.086 0.9313
GFR_MDRD -0.003536 0.002146 -1.648 0.0999 .
BMI -0.021052 0.010734 -1.961 0.0503 .
CAD_history -0.056364 0.092349 -0.610 0.5419
Stroke_history 0.187557 0.086643 2.165 0.0308 *
Peripheral.interv -0.051133 0.105339 -0.485 0.6276
stenose50-70% 0.012852 0.603337 0.021 0.9830
stenose70-90% 0.269820 0.576599 0.468 0.6400
stenose90-99% 0.105576 0.575918 0.183 0.8546
stenose100% (Occlusion) 0.179979 0.688069 0.262 0.7937
stenose70-99% -0.916356 0.912926 -1.004 0.3159
LDL_final -0.004545 0.040502 -0.112 0.9107
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9848 on 602 degrees of freedom
Multiple R-squared: 0.04836, Adjusted R-squared: 0.01832
F-statistic: 1.61 on 19 and 602 DF, p-value: 0.04862
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_rank ' with ' Macrophages_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_pg_ug_2015_rank
Trait/outcome.............: Macrophages_rank
Effect size...............: 0.087966
Standard error............: 0.040062
Odds ratio (effect size)..: 1.092
Lower 95% CI..............: 1.009
Upper 95% CI..............: 1.181
T-value...................: 2.19572
P-value...................: 0.02849284
R^2.......................: 0.048358
Adjusted r^2..............: 0.018322
Sample size of AE DB......: 2388
Sample size of model......: 622
Missing data %............: 73.9531
- processing SMC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + SmokerCurrent +
BMI + Stroke_history, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] SmokerCurrentyes BMI Stroke_history
0.38376 -0.16631 0.14266 -0.01639 0.19343
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose +
LDL_final, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.04985 -0.64800 0.01386 0.63069 2.55755
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.102447 0.806200 1.367 0.1720
currentDF[, TRAIT] -0.173938 0.040608 -4.283 2.14e-05 ***
Age -0.006913 0.004890 -1.414 0.1580
Gendermale -0.059486 0.089220 -0.667 0.5052
Hypertension.compositeyes -0.045232 0.121514 -0.372 0.7099
DiabetesStatusDiabetes -0.066154 0.099234 -0.667 0.5053
SmokerCurrentyes 0.118193 0.085988 1.375 0.1698
Med.Statin.LLDyes -0.100904 0.099815 -1.011 0.3125
Med.all.antiplateletyes -0.025880 0.139663 -0.185 0.8531
GFR_MDRD -0.003067 0.002130 -1.440 0.1505
BMI -0.018815 0.010660 -1.765 0.0781 .
CAD_history -0.032078 0.091688 -0.350 0.7266
Stroke_history 0.185386 0.085884 2.159 0.0313 *
Peripheral.interv -0.055377 0.105115 -0.527 0.5985
stenose50-70% 0.040206 0.597216 0.067 0.9463
stenose70-90% 0.333147 0.571076 0.583 0.5599
stenose90-99% 0.179920 0.570433 0.315 0.7526
stenose100% (Occlusion) 0.203774 0.680636 0.299 0.7647
stenose70-99% -0.753656 0.904215 -0.833 0.4049
LDL_final 0.008340 0.040306 0.207 0.8361
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9747 on 598 degrees of freedom
Multiple R-squared: 0.06837, Adjusted R-squared: 0.03877
F-statistic: 2.31 on 19 and 598 DF, p-value: 0.001317
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_rank ' with ' SMC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_pg_ug_2015_rank
Trait/outcome.............: SMC_rank
Effect size...............: -0.173938
Standard error............: 0.040608
Odds ratio (effect size)..: 0.84
Lower 95% CI..............: 0.776
Upper 95% CI..............: 0.91
T-value...................: -4.283342
P-value...................: 2.144872e-05
R^2.......................: 0.068374
Adjusted r^2..............: 0.038774
Sample size of AE DB......: 2388
Sample size of model......: 618
Missing data %............: 74.1206
- processing VesselDensity_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + SmokerCurrent +
GFR_MDRD + BMI + Stroke_history, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] SmokerCurrentyes GFR_MDRD BMI Stroke_history
0.645933 -0.073470 0.151037 -0.003299 -0.019370 0.223348
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose +
LDL_final, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.2477 -0.6315 0.0154 0.6292 2.7442
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.915562 0.833720 1.098 0.2726
currentDF[, TRAIT] -0.060043 0.044518 -1.349 0.1780
Age -0.002646 0.005072 -0.522 0.6020
Gendermale 0.006226 0.091559 0.068 0.9458
Hypertension.compositeyes -0.026768 0.129442 -0.207 0.8362
DiabetesStatusDiabetes -0.082035 0.106538 -0.770 0.4416
SmokerCurrentyes 0.129686 0.090901 1.427 0.1542
Med.Statin.LLDyes -0.102693 0.104321 -0.984 0.3253
Med.all.antiplateletyes 0.034800 0.147180 0.236 0.8132
GFR_MDRD -0.004247 0.002270 -1.871 0.0619 .
BMI -0.019937 0.011131 -1.791 0.0738 .
CAD_history -0.058081 0.096857 -0.600 0.5490
Stroke_history 0.204024 0.090661 2.250 0.0248 *
Peripheral.interv -0.088141 0.112268 -0.785 0.4327
stenose50-70% -0.120730 0.611812 -0.197 0.8436
stenose70-90% 0.168620 0.582906 0.289 0.7725
stenose90-99% 0.027030 0.581958 0.046 0.9630
stenose100% (Occlusion) 0.087610 0.694550 0.126 0.8997
stenose70-99% -1.032726 1.170442 -0.882 0.3780
LDL_final 0.013615 0.042281 0.322 0.7476
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9932 on 564 degrees of freedom
Multiple R-squared: 0.04632, Adjusted R-squared: 0.01419
F-statistic: 1.442 on 19 and 564 DF, p-value: 0.1012
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_rank ' with ' VesselDensity_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_pg_ug_2015_rank
Trait/outcome.............: VesselDensity_rank
Effect size...............: -0.060043
Standard error............: 0.044518
Odds ratio (effect size)..: 0.942
Lower 95% CI..............: 0.863
Upper 95% CI..............: 1.028
T-value...................: -1.348739
P-value...................: 0.1779623
R^2.......................: 0.04632
Adjusted r^2..............: 0.014193
Sample size of AE DB......: 2388
Sample size of model......: 584
Missing data %............: 75.54439
Analysis of IL6R_pg_ug_2015_rank.
- processing Macrophages_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Med.Statin.LLD + GFR_MDRD + Peripheral.interv, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age Med.Statin.LLDyes GFR_MDRD Peripheral.interv
1.341530 0.110321 -0.009911 -0.288745 -0.004870 0.266781
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose +
LDL_final, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.2981 -0.6084 -0.0185 0.6169 2.8340
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.427044 0.897757 1.590 0.11246
currentDF[, TRAIT] 0.108478 0.039229 2.765 0.00586 **
Age -0.009565 0.004928 -1.941 0.05274 .
Gendermale -0.033733 0.087882 -0.384 0.70123
Hypertension.compositeyes 0.062434 0.119466 0.523 0.60144
DiabetesStatusDiabetes -0.119784 0.100152 -1.196 0.23217
SmokerCurrentyes 0.038240 0.086309 0.443 0.65789
Med.Statin.LLDyes -0.285726 0.100574 -2.841 0.00465 **
Med.all.antiplateletyes -0.116713 0.142200 -0.821 0.41211
GFR_MDRD -0.005023 0.002183 -2.300 0.02177 *
BMI -0.013214 0.010896 -1.213 0.22570
CAD_history -0.028311 0.091591 -0.309 0.75735
Stroke_history 0.030194 0.086143 0.351 0.72608
Peripheral.interv 0.246999 0.105103 2.350 0.01910 *
stenose50-70% 0.061338 0.719052 0.085 0.93205
stenose70-90% 0.293751 0.696861 0.422 0.67352
stenose90-99% 0.380202 0.696101 0.546 0.58514
stenose100% (Occlusion) -0.089389 0.789470 -0.113 0.90989
stenose70-99% -1.096271 0.985446 -1.112 0.26639
LDL_final 0.006939 0.040808 0.170 0.86504
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9713 on 596 degrees of freedom
Multiple R-squared: 0.07061, Adjusted R-squared: 0.04099
F-statistic: 2.383 on 19 and 596 DF, p-value: 0.0008684
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_rank ' with ' Macrophages_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6R_pg_ug_2015_rank
Trait/outcome.............: Macrophages_rank
Effect size...............: 0.108478
Standard error............: 0.039229
Odds ratio (effect size)..: 1.115
Lower 95% CI..............: 1.032
Upper 95% CI..............: 1.204
T-value...................: 2.76527
P-value...................: 0.005863709
R^2.......................: 0.070614
Adjusted r^2..............: 0.040986
Sample size of AE DB......: 2388
Sample size of model......: 616
Missing data %............: 74.20436
- processing SMC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Age + Med.Statin.LLD + GFR_MDRD +
Peripheral.interv, data = currentDF)
Coefficients:
(Intercept) Age Med.Statin.LLDyes GFR_MDRD Peripheral.interv
1.346173 -0.010035 -0.298570 -0.004661 0.264690
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose +
LDL_final, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.4215 -0.6309 -0.0031 0.6277 2.8688
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.4259326 0.9036264 1.578 0.1151
currentDF[, TRAIT] 0.0451999 0.0409217 1.105 0.2698
Age -0.0094317 0.0050198 -1.879 0.0608 .
Gendermale 0.0053504 0.0896154 0.060 0.9524
Hypertension.compositeyes 0.0684131 0.1202476 0.569 0.5696
DiabetesStatusDiabetes -0.1253518 0.1008768 -1.243 0.2145
SmokerCurrentyes 0.0244030 0.0870966 0.280 0.7794
Med.Statin.LLDyes -0.3010755 0.1017199 -2.960 0.0032 **
Med.all.antiplateletyes -0.1322906 0.1430097 -0.925 0.3553
GFR_MDRD -0.0048356 0.0022028 -2.195 0.0285 *
BMI -0.0122421 0.0110041 -1.113 0.2664
CAD_history -0.0269855 0.0924033 -0.292 0.7704
Stroke_history 0.0404923 0.0868126 0.466 0.6411
Peripheral.interv 0.2464366 0.1066174 2.311 0.0212 *
stenose50-70% 0.0201160 0.7239258 0.028 0.9778
stenose70-90% 0.2720544 0.7017170 0.388 0.6984
stenose90-99% 0.3421500 0.7010877 0.488 0.6257
stenose100% (Occlusion) -0.1850207 0.7946637 -0.233 0.8160
stenose70-99% -1.1033831 0.9923601 -1.112 0.2666
LDL_final 0.0001877 0.0413000 0.005 0.9964
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9772 on 592 degrees of freedom
Multiple R-squared: 0.05852, Adjusted R-squared: 0.02831
F-statistic: 1.937 on 19 and 592 DF, p-value: 0.009947
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_rank ' with ' SMC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6R_pg_ug_2015_rank
Trait/outcome.............: SMC_rank
Effect size...............: 0.0452
Standard error............: 0.040922
Odds ratio (effect size)..: 1.046
Lower 95% CI..............: 0.966
Upper 95% CI..............: 1.134
T-value...................: 1.104546
P-value...................: 0.269805
R^2.......................: 0.058524
Adjusted r^2..............: 0.028307
Sample size of AE DB......: 2388
Sample size of model......: 612
Missing data %............: 74.37186
- processing VesselDensity_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Med.Statin.LLD + GFR_MDRD + Peripheral.interv, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age Med.Statin.LLDyes GFR_MDRD Peripheral.interv
1.401930 0.070985 -0.010425 -0.313222 -0.004864 0.237237
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose +
LDL_final, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.3090 -0.6200 -0.0146 0.6457 2.8167
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.509171 0.924131 1.633 0.10302
currentDF[, TRAIT] 0.067452 0.045210 1.492 0.13627
Age -0.010954 0.005160 -2.123 0.03422 *
Gendermale -0.030480 0.091431 -0.333 0.73899
Hypertension.compositeyes 0.052927 0.127452 0.415 0.67810
DiabetesStatusDiabetes -0.137947 0.107477 -1.283 0.19985
SmokerCurrentyes -0.005957 0.091386 -0.065 0.94805
Med.Statin.LLDyes -0.302578 0.105563 -2.866 0.00431 **
Med.all.antiplateletyes -0.106337 0.149561 -0.711 0.47739
GFR_MDRD -0.005046 0.002339 -2.157 0.03143 *
BMI -0.012429 0.011509 -1.080 0.28064
CAD_history -0.038156 0.096794 -0.394 0.69359
Stroke_history 0.024410 0.090867 0.269 0.78831
Peripheral.interv 0.225570 0.112822 1.999 0.04606 *
stenose50-70% 0.068589 0.733201 0.094 0.92550
stenose70-90% 0.326386 0.708414 0.461 0.64517
stenose90-99% 0.395119 0.707544 0.558 0.57677
stenose100% (Occlusion) -0.102343 0.802434 -0.128 0.89856
stenose70-99% -1.253510 1.232215 -1.017 0.30946
LDL_final 0.009660 0.042947 0.225 0.82212
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9861 on 557 degrees of freedom
Multiple R-squared: 0.06024, Adjusted R-squared: 0.02818
F-statistic: 1.879 on 19 and 557 DF, p-value: 0.01347
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_rank ' with ' VesselDensity_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6R_pg_ug_2015_rank
Trait/outcome.............: VesselDensity_rank
Effect size...............: 0.067452
Standard error............: 0.04521
Odds ratio (effect size)..: 1.07
Lower 95% CI..............: 0.979
Upper 95% CI..............: 1.169
T-value...................: 1.49198
P-value...................: 0.1362705
R^2.......................: 0.060239
Adjusted r^2..............: 0.028183
Sample size of AE DB......: 2388
Sample size of model......: 577
Missing data %............: 75.83752
Analysis of MCP1_pg_ug_2015_rank.
- processing Macrophages_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Gender + Hypertension.composite +
Med.Statin.LLD + Stroke_history + LDL_final, data = currentDF)
Coefficients:
(Intercept) Gendermale Hypertension.compositeyes Med.Statin.LLDyes Stroke_history
-0.1273 0.1474 -0.2463 -0.1445 0.1993
LDL_final
0.1006
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose +
LDL_final, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.9304 -0.7061 -0.0780 0.6314 3.2286
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.291415 0.850113 0.343 0.7319
currentDF[, TRAIT] -0.049187 0.041051 -1.198 0.2313
Age -0.003564 0.005088 -0.700 0.4839
Gendermale 0.158377 0.091362 1.734 0.0835 .
Hypertension.compositeyes -0.244154 0.125233 -1.950 0.0517 .
DiabetesStatusDiabetes -0.068577 0.103560 -0.662 0.5081
SmokerCurrentyes -0.074855 0.089571 -0.836 0.4036
Med.Statin.LLDyes -0.159345 0.104268 -1.528 0.1270
Med.all.antiplateletyes -0.108230 0.145261 -0.745 0.4565
GFR_MDRD -0.002234 0.002227 -1.003 0.3161
BMI -0.008739 0.011066 -0.790 0.4300
CAD_history 0.071133 0.094850 0.750 0.4536
Stroke_history 0.213464 0.090021 2.371 0.0180 *
Peripheral.interv 0.065349 0.109169 0.599 0.5497
stenose50-70% 0.098476 0.631189 0.156 0.8761
stenose70-90% 0.387475 0.603162 0.642 0.5208
stenose90-99% 0.271932 0.602534 0.451 0.6519
stenose100% (Occlusion) -0.341989 0.719678 -0.475 0.6348
stenose70-99% 0.492227 0.954935 0.515 0.6064
LDL_final 0.105914 0.042067 2.518 0.0121 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.03 on 618 degrees of freedom
Multiple R-squared: 0.05515, Adjusted R-squared: 0.02611
F-statistic: 1.899 on 19 and 618 DF, p-value: 0.01201
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' Macrophages_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: Macrophages_rank
Effect size...............: -0.049187
Standard error............: 0.041051
Odds ratio (effect size)..: 0.952
Lower 95% CI..............: 0.878
Upper 95% CI..............: 1.032
T-value...................: -1.198196
P-value...................: 0.2313002
R^2.......................: 0.055154
Adjusted r^2..............: 0.026106
Sample size of AE DB......: 2388
Sample size of model......: 638
Missing data %............: 73.28308
- processing SMC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Hypertension.composite +
Stroke_history + LDL_final, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Hypertension.compositeyes Stroke_history LDL_final
-0.1782 -0.1303 -0.2508 0.1852 0.1236
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose +
LDL_final, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.6887 -0.6991 -0.0832 0.6479 3.2672
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.320457 0.847551 0.378 0.70549
currentDF[, TRAIT] -0.123250 0.042025 -2.933 0.00349 **
Age -0.005426 0.005127 -1.058 0.29029
Gendermale 0.106151 0.092455 1.148 0.25136
Hypertension.compositeyes -0.244452 0.124883 -1.957 0.05075 .
DiabetesStatusDiabetes -0.062338 0.103312 -0.603 0.54647
SmokerCurrentyes -0.072628 0.089528 -0.811 0.41755
Med.Statin.LLDyes -0.148656 0.104496 -1.423 0.15536
Med.all.antiplateletyes -0.119560 0.144832 -0.826 0.40940
GFR_MDRD -0.002072 0.002227 -0.931 0.35241
BMI -0.007669 0.011068 -0.693 0.48861
CAD_history 0.075659 0.094760 0.798 0.42493
Stroke_history 0.205614 0.089880 2.288 0.02250 *
Peripheral.interv 0.067705 0.109692 0.617 0.53732
stenose50-70% 0.167650 0.629251 0.266 0.79000
stenose70-90% 0.459421 0.601627 0.764 0.44538
stenose90-99% 0.359549 0.601076 0.598 0.54994
stenose100% (Occlusion) -0.233259 0.716966 -0.325 0.74503
stenose70-99% 0.599918 0.952635 0.630 0.52909
LDL_final 0.114486 0.042160 2.716 0.00680 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.027 on 614 degrees of freedom
Multiple R-squared: 0.06606, Adjusted R-squared: 0.03716
F-statistic: 2.286 on 19 and 614 DF, p-value: 0.001497
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' SMC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: SMC_rank
Effect size...............: -0.12325
Standard error............: 0.042025
Odds ratio (effect size)..: 0.884
Lower 95% CI..............: 0.814
Upper 95% CI..............: 0.96
T-value...................: -2.932747
P-value...................: 0.003485007
R^2.......................: 0.066063
Adjusted r^2..............: 0.037163
Sample size of AE DB......: 2388
Sample size of model......: 634
Missing data %............: 73.45059
- processing VesselDensity_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
Hypertension.composite + GFR_MDRD + Stroke_history + LDL_final,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale Hypertension.compositeyes GFR_MDRD
-0.187163 -0.136560 0.195759 -0.227145 -0.003343
Stroke_history LDL_final
0.210043 0.141315
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose +
LDL_final, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.7564 -0.7060 -0.0830 0.6373 3.3215
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.278174 0.861772 0.323 0.74697
currentDF[, TRAIT] -0.131364 0.045384 -2.894 0.00394 **
Age -0.002927 0.005233 -0.559 0.57613
Gendermale 0.185450 0.093464 1.984 0.04771 *
Hypertension.compositeyes -0.202835 0.131290 -1.545 0.12291
DiabetesStatusDiabetes -0.057482 0.108946 -0.528 0.59796
SmokerCurrentyes -0.044276 0.093160 -0.475 0.63477
Med.Statin.LLDyes -0.155652 0.107545 -1.447 0.14835
Med.all.antiplateletyes -0.066229 0.149739 -0.442 0.65844
GFR_MDRD -0.002916 0.002332 -1.250 0.21165
BMI -0.010907 0.011460 -0.952 0.34161
CAD_history 0.089160 0.098346 0.907 0.36500
Stroke_history 0.229929 0.093253 2.466 0.01397 *
Peripheral.interv 0.029074 0.115049 0.253 0.80058
stenose50-70% -0.066620 0.633787 -0.105 0.91632
stenose70-90% 0.276392 0.603793 0.458 0.64730
stenose90-99% 0.171897 0.602917 0.285 0.77566
stenose100% (Occlusion) -0.398319 0.719404 -0.554 0.58001
stenose70-99% 0.622479 1.212095 0.514 0.60776
LDL_final 0.122158 0.043471 2.810 0.00512 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.029 on 579 degrees of freedom
Multiple R-squared: 0.07479, Adjusted R-squared: 0.04443
F-statistic: 2.463 on 19 and 579 DF, p-value: 0.0005535
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' VesselDensity_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: VesselDensity_rank
Effect size...............: -0.131364
Standard error............: 0.045384
Odds ratio (effect size)..: 0.877
Lower 95% CI..............: 0.802
Upper 95% CI..............: 0.958
T-value...................: -2.894467
P-value...................: 0.003941151
R^2.......................: 0.074786
Adjusted r^2..............: 0.044425
Sample size of AE DB......: 2388
Sample size of model......: 599
Missing data %............: 74.91625 cat("Edit the column names...\n")Edit the column names...colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
"Beta", "s.e.m.",
"OR", "low95CI", "up95CI",
"T-value", "P-value", "r^2", "r^2_adj", "AE_N", "Model_N", "Perc_Miss")
cat("Correct the variable types...\n")Correct the variable types...GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)
# Save the data
cat("Writing results to Excel-file...\n")Writing results to Excel-file...### Univariate
library(openxlsx)
write.xlsx(GLM.results,
file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Con.Multi.Protein.PlaquePhenotypes.RANK.MODEL3.xlsx"),
row.names = FALSE, col.names = TRUE, sheetName = "Con.Multi.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")Removing intermediate files...rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)
Analysis of binary plaque traits as a function of serum/plaque IL6(R)/MCP1 levels.
GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
PROTEIN = TRAITS.PROTEIN.RANK[protein]
cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
for (trait in 1:length(TRAITS.BIN)) {
TRAIT = TRAITS.BIN[trait]
cat(paste0("\n- processing ",TRAIT,"\n\n"))
currentDF <- as.data.frame(AEDB.CEA %>%
dplyr::select(., PROTEIN, TRAIT, COVARIATES_M3) %>%
filter(complete.cases(.))) %>%
filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
# for debug
# print(DT::datatable(currentDF))
# print(nrow(currentDF))
# print(str(currentDF))
# print(class(currentDF[,TRAIT]))
### univariate
fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender +
Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose + LDL_final,
data = currentDF, family = binomial(link = "logit"))
model_step <- stepAIC(fit, direction = "both", trace = FALSE)
print(model_step)
print(summary(fit))
GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
GLM.results = rbind(GLM.results, GLM.results.TEMP)
}
}
Analysis of IL6_rank.
- processing CalcificationPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Hypertension.composite +
Peripheral.interv + LDL_final, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) Hypertension.compositeyes Peripheral.interv LDL_final
0.1729 0.7282 -0.4273 -0.1618
Degrees of Freedom: 340 Total (i.e. Null); 337 Residual
Null Deviance: 468.3
Residual Deviance: 457.8 AIC: 465.8
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + LDL_final, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.8654 -1.2136 0.7951 1.0480 1.7633
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -15.267781 535.414752 -0.029 0.9773
currentDF[, PROTEIN] 0.163515 0.117905 1.387 0.1655
Age 0.010455 0.014857 0.704 0.4816
Gendermale -0.035222 0.266332 -0.132 0.8948
Hypertension.compositeyes 0.786773 0.346070 2.273 0.0230 *
DiabetesStatusDiabetes -0.344363 0.306681 -1.123 0.2615
SmokerCurrentyes 0.053976 0.246235 0.219 0.8265
Med.Statin.LLDyes -0.175548 0.281406 -0.624 0.5327
Med.all.antiplateletyes 0.893264 0.512220 1.744 0.0812 .
GFR_MDRD -0.006645 0.006908 -0.962 0.3361
BMI 0.021593 0.031868 0.678 0.4981
CAD_history 0.085080 0.263727 0.323 0.7470
Stroke_history -0.294773 0.250322 -1.178 0.2390
Peripheral.interv -0.398990 0.287559 -1.388 0.1653
stenose50-70% 14.702266 535.411824 0.027 0.9781
stenose70-90% 14.250535 535.411403 0.027 0.9788
stenose90-99% 13.920079 535.411392 0.026 0.9793
stenose100% (Occlusion) 15.923187 535.412811 0.030 0.9763
LDL_final -0.183427 0.121427 -1.511 0.1309
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 468.26 on 340 degrees of freedom
Residual deviance: 443.77 on 322 degrees of freedom
AIC: 481.77
Number of Fisher Scoring iterations: 12
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_rank ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_rank
Trait/outcome.............: CalcificationPlaque
Effect size...............: 0.163515
Standard error............: 0.117905
Odds ratio (effect size)..: 1.178
Lower 95% CI..............: 0.935
Upper 95% CI..............: 1.484
Z-value...................: 1.386835
P-value...................: 0.1654921
Hosmer and Lemeshow r^2...: 0.05229
Cox and Snell r^2.........: 0.069286
Nagelkerke's pseudo r^2...: 0.09279
Sample size of AE DB......: 2388
Sample size of model......: 341
Missing data %............: 85.72027
- processing CollagenPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ stenose, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) stenose50-70% stenose70-90% stenose90-99% stenose100% (Occlusion)
1.757e+01 1.794e-08 -1.602e+01 -1.639e+01 1.752e-08
Degrees of Freedom: 339 Total (i.e. Null); 335 Residual
Null Deviance: 343
Residual Deviance: 333.4 AIC: 343.4
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + LDL_final, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.12359 0.00031 0.59757 0.72715 1.08348
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.825e+01 3.956e+03 0.005 0.996
currentDF[, PROTEIN] -1.729e-01 1.444e-01 -1.198 0.231
Age 6.532e-03 1.853e-02 0.353 0.724
Gendermale -3.604e-01 3.433e-01 -1.050 0.294
Hypertension.compositeyes 3.197e-01 4.074e-01 0.785 0.433
DiabetesStatusDiabetes 3.033e-01 4.040e-01 0.751 0.453
SmokerCurrentyes 3.532e-01 3.085e-01 1.145 0.252
Med.Statin.LLDyes -1.702e-01 3.466e-01 -0.491 0.623
Med.all.antiplateletyes 7.388e-01 5.520e-01 1.338 0.181
GFR_MDRD -5.268e-03 8.525e-03 -0.618 0.537
BMI -2.169e-02 3.918e-02 -0.554 0.580
CAD_history -5.403e-02 3.206e-01 -0.169 0.866
Stroke_history 2.122e-01 3.180e-01 0.667 0.505
Peripheral.interv -4.147e-01 3.348e-01 -1.239 0.215
stenose50-70% -5.289e-01 4.127e+03 0.000 1.000
stenose70-90% -1.652e+01 3.956e+03 -0.004 0.997
stenose90-99% -1.702e+01 3.956e+03 -0.004 0.997
stenose100% (Occlusion) -2.020e-01 4.310e+03 0.000 1.000
LDL_final -1.001e-01 1.464e-01 -0.684 0.494
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 343.03 on 339 degrees of freedom
Residual deviance: 322.63 on 321 degrees of freedom
AIC: 360.63
Number of Fisher Scoring iterations: 16
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_rank ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_rank
Trait/outcome.............: CollagenPlaque
Effect size...............: -0.172899
Standard error............: 0.144367
Odds ratio (effect size)..: 0.841
Lower 95% CI..............: 0.634
Upper 95% CI..............: 1.116
Z-value...................: -1.197636
P-value...................: 0.231059
Hosmer and Lemeshow r^2...: 0.059465
Cox and Snell r^2.........: 0.05823
Nagelkerke's pseudo r^2...: 0.091646
Sample size of AE DB......: 2388
Sample size of model......: 340
Missing data %............: 85.76214
- processing Fat10Perc
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Gender + Hypertension.composite +
DiabetesStatus + Stroke_history + LDL_final, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) Gendermale Hypertension.compositeyes DiabetesStatusDiabetes Stroke_history
-0.8534 1.0245 0.9471 -0.5168 0.4840
LDL_final
0.2434
Degrees of Freedom: 340 Total (i.e. Null); 335 Residual
Null Deviance: 343.5
Residual Deviance: 321.4 AIC: 333.4
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + LDL_final, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.6383 0.3499 0.5062 0.6887 1.3371
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 10.524691 882.747063 0.012 0.990487
currentDF[, PROTEIN] 0.154515 0.145906 1.059 0.289599
Age -0.004901 0.018852 -0.260 0.794872
Gendermale 1.227524 0.325007 3.777 0.000159 ***
Hypertension.compositeyes 0.930452 0.395899 2.350 0.018762 *
DiabetesStatusDiabetes -0.574189 0.363916 -1.578 0.114610
SmokerCurrentyes 0.281302 0.317408 0.886 0.375485
Med.Statin.LLDyes -0.083895 0.371198 -0.226 0.821192
Med.all.antiplateletyes 0.917466 0.569904 1.610 0.107428
GFR_MDRD -0.007444 0.009202 -0.809 0.418546
BMI 0.030642 0.040203 0.762 0.445961
CAD_history -0.109329 0.335226 -0.326 0.744322
Stroke_history 0.427611 0.334719 1.278 0.201418
Peripheral.interv -0.220845 0.349112 -0.633 0.527001
stenose50-70% -13.821360 882.743866 -0.016 0.987508
stenose70-90% -12.101714 882.743616 -0.014 0.989062
stenose90-99% -12.351083 882.743599 -0.014 0.988837
stenose100% (Occlusion) -11.833540 882.744465 -0.013 0.989304
LDL_final 0.273940 0.165892 1.651 0.098674 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 343.48 on 340 degrees of freedom
Residual deviance: 309.36 on 322 degrees of freedom
AIC: 347.36
Number of Fisher Scoring iterations: 13
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_rank ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_rank
Trait/outcome.............: Fat10Perc
Effect size...............: 0.154515
Standard error............: 0.145906
Odds ratio (effect size)..: 1.167
Lower 95% CI..............: 0.877
Upper 95% CI..............: 1.553
Z-value...................: 1.059002
P-value...................: 0.289599
Hosmer and Lemeshow r^2...: 0.09933
Cox and Snell r^2.........: 0.09521
Nagelkerke's pseudo r^2...: 0.149988
Sample size of AE DB......: 2388
Sample size of model......: 341
Missing data %............: 85.72027
- processing IPH
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + Gender +
DiabetesStatus, family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) Age Gendermale DiabetesStatusDiabetes
-1.97984 0.04335 0.40381 -0.57855
Degrees of Freedom: 340 Total (i.e. Null); 337 Residual
Null Deviance: 393.6
Residual Deviance: 377.1 AIC: 385.1
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + LDL_final, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.1786 -1.1062 0.6502 0.8046 1.3778
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 8.507942 882.746215 0.010 0.99231
currentDF[, PROTEIN] 0.053103 0.130729 0.406 0.68459
Age 0.044678 0.016690 2.677 0.00743 **
Gendermale 0.518684 0.290082 1.788 0.07377 .
Hypertension.compositeyes 0.427483 0.368220 1.161 0.24566
DiabetesStatusDiabetes -0.639149 0.323487 -1.976 0.04818 *
SmokerCurrentyes 0.083062 0.277050 0.300 0.76432
Med.Statin.LLDyes 0.030957 0.319436 0.097 0.92280
Med.all.antiplateletyes 0.372978 0.547031 0.682 0.49535
GFR_MDRD -0.002094 0.007832 -0.267 0.78923
BMI 0.023163 0.035168 0.659 0.51012
CAD_history 0.100163 0.302999 0.331 0.74097
Stroke_history 0.111451 0.285723 0.390 0.69649
Peripheral.interv 0.448655 0.342139 1.311 0.18975
stenose50-70% -12.121321 882.743894 -0.014 0.98904
stenose70-90% -12.421281 882.743569 -0.014 0.98877
stenose90-99% -12.359348 882.743564 -0.014 0.98883
stenose100% (Occlusion) -11.541968 882.744409 -0.013 0.98957
LDL_final 0.114250 0.139299 0.820 0.41212
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 393.60 on 340 degrees of freedom
Residual deviance: 371.24 on 322 degrees of freedom
AIC: 409.24
Number of Fisher Scoring iterations: 13
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_rank ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_rank
Trait/outcome.............: IPH
Effect size...............: 0.053103
Standard error............: 0.130729
Odds ratio (effect size)..: 1.055
Lower 95% CI..............: 0.816
Upper 95% CI..............: 1.363
Z-value...................: 0.406207
P-value...................: 0.6845907
Hosmer and Lemeshow r^2...: 0.056802
Cox and Snell r^2.........: 0.06346
Nagelkerke's pseudo r^2...: 0.092683
Sample size of AE DB......: 2388
Sample size of model......: 341
Missing data %............: 85.72027
Analysis of MCP1_rank.
- processing CalcificationPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Hypertension.composite +
DiabetesStatus + Peripheral.interv + LDL_final, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) Hypertension.compositeyes DiabetesStatusDiabetes Peripheral.interv LDL_final
0.4110 0.6502 -0.4656 -0.4820 -0.1722
Degrees of Freedom: 361 Total (i.e. Null); 357 Residual
Null Deviance: 494.9
Residual Deviance: 482.9 AIC: 492.9
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + LDL_final, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.8997 -1.1747 0.7992 1.0384 1.7588
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -14.541447 535.414250 -0.027 0.9783
currentDF[, PROTEIN] -0.042755 0.110886 -0.386 0.6998
Age 0.014087 0.014347 0.982 0.3261
Gendermale 0.015604 0.259328 0.060 0.9520
Hypertension.compositeyes 0.674571 0.325086 2.075 0.0380 *
DiabetesStatusDiabetes -0.521153 0.295618 -1.763 0.0779 .
SmokerCurrentyes 0.243944 0.239906 1.017 0.3092
Med.Statin.LLDyes -0.321147 0.274334 -1.171 0.2417
Med.all.antiplateletyes 0.693103 0.464849 1.491 0.1360
GFR_MDRD -0.005488 0.006663 -0.824 0.4102
BMI 0.017848 0.029365 0.608 0.5433
CAD_history 0.019152 0.258874 0.074 0.9410
Stroke_history -0.354716 0.242260 -1.464 0.1431
Peripheral.interv -0.474133 0.289636 -1.637 0.1016
stenose50-70% 14.148656 535.411734 0.026 0.9789
stenose70-90% 13.843373 535.411340 0.026 0.9794
stenose90-99% 13.476392 535.411334 0.025 0.9799
stenose100% (Occlusion) 14.975966 535.412896 0.028 0.9777
LDL_final -0.214336 0.117309 -1.827 0.0677 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 494.91 on 361 degrees of freedom
Residual deviance: 470.68 on 343 degrees of freedom
AIC: 508.68
Number of Fisher Scoring iterations: 12
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: CalcificationPlaque
Effect size...............: -0.042755
Standard error............: 0.110886
Odds ratio (effect size)..: 0.958
Lower 95% CI..............: 0.771
Upper 95% CI..............: 1.191
Z-value...................: -0.385574
P-value...................: 0.6998121
Hosmer and Lemeshow r^2...: 0.04895
Cox and Snell r^2.........: 0.064732
Nagelkerke's pseudo r^2...: 0.086869
Sample size of AE DB......: 2388
Sample size of model......: 362
Missing data %............: 84.84087
- processing CollagenPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Med.all.antiplatelet + stenose, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Med.all.antiplateletyes stenose50-70% stenose70-90%
17.1818 -0.6076 0.9495 -0.2888 -16.2141
stenose90-99% stenose100% (Occlusion)
-16.8072 -0.1013
Degrees of Freedom: 360 Total (i.e. Null); 354 Residual
Null Deviance: 366.2
Residual Deviance: 333.1 AIC: 347.1
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + LDL_final, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.4374 0.2133 0.5256 0.7029 1.4457
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.747e+01 3.956e+03 0.004 0.9965
currentDF[, PROTEIN] -6.062e-01 1.487e-01 -4.078 4.55e-05 ***
Age 5.814e-03 1.840e-02 0.316 0.7520
Gendermale -1.376e-01 3.454e-01 -0.398 0.6904
Hypertension.compositeyes 3.582e-01 3.944e-01 0.908 0.3637
DiabetesStatusDiabetes 4.016e-01 4.067e-01 0.987 0.3234
SmokerCurrentyes 3.919e-01 3.061e-01 1.280 0.2004
Med.Statin.LLDyes -4.142e-02 3.399e-01 -0.122 0.9030
Med.all.antiplateletyes 1.039e+00 5.400e-01 1.924 0.0544 .
GFR_MDRD -6.003e-03 8.531e-03 -0.704 0.4816
BMI -1.599e-02 3.926e-02 -0.407 0.6837
CAD_history -5.400e-02 3.195e-01 -0.169 0.8658
Stroke_history 2.244e-01 3.125e-01 0.718 0.4728
Peripheral.interv -3.738e-01 3.532e-01 -1.058 0.2900
stenose50-70% -4.501e-01 4.122e+03 0.000 0.9999
stenose70-90% -1.648e+01 3.956e+03 -0.004 0.9967
stenose90-99% -1.712e+01 3.956e+03 -0.004 0.9965
stenose100% (Occlusion) -2.379e-01 4.336e+03 0.000 1.0000
LDL_final 4.524e-03 1.476e-01 0.031 0.9755
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 366.23 on 360 degrees of freedom
Residual deviance: 326.43 on 342 degrees of freedom
AIC: 364.43
Number of Fisher Scoring iterations: 16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: CollagenPlaque
Effect size...............: -0.606247
Standard error............: 0.148674
Odds ratio (effect size)..: 0.545
Lower 95% CI..............: 0.408
Upper 95% CI..............: 0.73
Z-value...................: -4.077683
P-value...................: 4.548676e-05
Hosmer and Lemeshow r^2...: 0.108674
Cox and Snell r^2.........: 0.104387
Nagelkerke's pseudo r^2...: 0.163768
Sample size of AE DB......: 2388
Sample size of model......: 361
Missing data %............: 84.88275
- processing Fat10Perc
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Gender + Hypertension.composite + SmokerCurrent + stenose +
LDL_final, family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Gendermale Hypertension.compositeyes SmokerCurrentyes
11.7736 0.5101 0.9142 0.9542 0.4421
stenose50-70% stenose70-90% stenose90-99% stenose100% (Occlusion) LDL_final
-14.0712 -12.1842 -12.7510 -12.4564 0.2372
Degrees of Freedom: 361 Total (i.e. Null); 352 Residual
Null Deviance: 361.2
Residual Deviance: 321.1 AIC: 341.1
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + LDL_final, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.3842 0.2960 0.4886 0.6684 1.6498
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 10.395966 882.746608 0.012 0.99060
currentDF[, PROTEIN] 0.498113 0.150588 3.308 0.00094 ***
Age 0.002141 0.018255 0.117 0.90662
Gendermale 0.936906 0.314287 2.981 0.00287 **
Hypertension.compositeyes 0.962021 0.389396 2.471 0.01349 *
DiabetesStatusDiabetes -0.217385 0.369876 -0.588 0.55672
SmokerCurrentyes 0.421196 0.313609 1.343 0.17925
Med.Statin.LLDyes -0.038865 0.357900 -0.109 0.91353
Med.all.antiplateletyes 0.483579 0.553916 0.873 0.38265
GFR_MDRD -0.003421 0.008894 -0.385 0.70053
BMI 0.026465 0.037523 0.705 0.48062
CAD_history -0.069242 0.335113 -0.207 0.83631
Stroke_history 0.332254 0.328273 1.012 0.31148
Peripheral.interv -0.104067 0.358243 -0.290 0.77144
stenose50-70% -13.706793 882.743836 -0.016 0.98761
stenose70-90% -11.838995 882.743573 -0.013 0.98930
stenose90-99% -12.435399 882.743559 -0.014 0.98876
stenose100% (Occlusion) -11.892994 882.744422 -0.013 0.98925
LDL_final 0.237520 0.160466 1.480 0.13882
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 361.18 on 361 degrees of freedom
Residual deviance: 317.86 on 343 degrees of freedom
AIC: 355.86
Number of Fisher Scoring iterations: 13
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: Fat10Perc
Effect size...............: 0.498113
Standard error............: 0.150588
Odds ratio (effect size)..: 1.646
Lower 95% CI..............: 1.225
Upper 95% CI..............: 2.211
Z-value...................: 3.30779
P-value...................: 0.0009403531
Hosmer and Lemeshow r^2...: 0.119926
Cox and Snell r^2.........: 0.112773
Nagelkerke's pseudo r^2...: 0.17864
Sample size of AE DB......: 2388
Sample size of model......: 362
Missing data %............: 84.84087
- processing IPH
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + Gender +
DiabetesStatus + Peripheral.interv, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) Age Gendermale DiabetesStatusDiabetes Peripheral.interv
-1.38377 0.03137 0.57820 -0.59140 0.47188
Degrees of Freedom: 361 Total (i.e. Null); 357 Residual
Null Deviance: 414.6
Residual Deviance: 398.2 AIC: 408.2
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + LDL_final, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.1230 -1.1154 0.6446 0.8048 1.3790
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 9.817110 882.745818 0.011 0.9911
currentDF[, PROTEIN] 0.021154 0.126653 0.167 0.8673
Age 0.029966 0.016143 1.856 0.0634 .
Gendermale 0.615473 0.280452 2.195 0.0282 *
Hypertension.compositeyes 0.351264 0.354391 0.991 0.3216
DiabetesStatusDiabetes -0.578653 0.312855 -1.850 0.0644 .
SmokerCurrentyes 0.118988 0.270115 0.441 0.6596
Med.Statin.LLDyes -0.164474 0.314860 -0.522 0.6014
Med.all.antiplateletyes 0.451435 0.498323 0.906 0.3650
GFR_MDRD -0.001996 0.007621 -0.262 0.7934
BMI 0.019887 0.032441 0.613 0.5399
CAD_history 0.239472 0.302172 0.793 0.4281
Stroke_history 0.199976 0.278586 0.718 0.4729
Peripheral.interv 0.451404 0.347558 1.299 0.1940
stenose50-70% -12.013293 882.743893 -0.014 0.9891
stenose70-90% -12.605523 882.743522 -0.014 0.9886
stenose90-99% -12.575565 882.743518 -0.014 0.9886
stenose100% (Occlusion) -12.140411 882.744417 -0.014 0.9890
LDL_final 0.091522 0.135650 0.675 0.4999
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 414.64 on 361 degrees of freedom
Residual deviance: 392.62 on 343 degrees of freedom
AIC: 430.62
Number of Fisher Scoring iterations: 13
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: IPH
Effect size...............: 0.021154
Standard error............: 0.126653
Odds ratio (effect size)..: 1.021
Lower 95% CI..............: 0.797
Upper 95% CI..............: 1.309
Z-value...................: 0.167027
P-value...................: 0.867349
Hosmer and Lemeshow r^2...: 0.053111
Cox and Snell r^2.........: 0.059021
Nagelkerke's pseudo r^2...: 0.086553
Sample size of AE DB......: 2388
Sample size of model......: 362
Missing data %............: 84.84087
Analysis of IL6_pg_ug_2015_rank.
- processing CalcificationPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + SmokerCurrent +
LDL_final, family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) Age SmokerCurrentyes LDL_final
-1.27196 0.02486 0.41455 -0.16393
Degrees of Freedom: 619 Total (i.e. Null); 616 Residual
Null Deviance: 857.9
Residual Deviance: 843 AIC: 851
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + LDL_final, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.7301 -1.1761 0.8192 1.0957 1.7649
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -1.747181 1.735445 -1.007 0.31405
currentDF[, PROTEIN] -0.035015 0.084708 -0.413 0.67934
Age 0.029151 0.010296 2.831 0.00464 **
Gendermale 0.053398 0.184699 0.289 0.77250
Hypertension.compositeyes 0.190931 0.256757 0.744 0.45710
DiabetesStatusDiabetes -0.117544 0.208989 -0.562 0.57381
SmokerCurrentyes 0.473160 0.183544 2.578 0.00994 **
Med.Statin.LLDyes -0.273095 0.211314 -1.292 0.19623
Med.all.antiplateletyes 0.134078 0.293577 0.457 0.64788
GFR_MDRD 0.005082 0.004527 1.123 0.26159
BMI 0.018661 0.022444 0.831 0.40570
CAD_history 0.018320 0.193210 0.095 0.92446
Stroke_history -0.225916 0.181521 -1.245 0.21329
Peripheral.interv -0.330372 0.219549 -1.505 0.13238
stenose50-70% -1.299569 1.303726 -0.997 0.31886
stenose70-90% -0.544255 1.245214 -0.437 0.66205
stenose90-99% -0.447456 1.243621 -0.360 0.71900
stenose100% (Occlusion) 0.159048 1.512113 0.105 0.91623
stenose70-99% -15.712343 605.394374 -0.026 0.97929
LDL_final -0.209060 0.086575 -2.415 0.01574 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 857.85 on 619 degrees of freedom
Residual deviance: 824.90 on 600 degrees of freedom
AIC: 864.9
Number of Fisher Scoring iterations: 13
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_rank ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_pg_ug_2015_rank
Trait/outcome.............: CalcificationPlaque
Effect size...............: -0.035015
Standard error............: 0.084708
Odds ratio (effect size)..: 0.966
Lower 95% CI..............: 0.818
Upper 95% CI..............: 1.14
Z-value...................: -0.413367
P-value...................: 0.679338
Hosmer and Lemeshow r^2...: 0.03841
Cox and Snell r^2.........: 0.051757
Nagelkerke's pseudo r^2...: 0.069071
Sample size of AE DB......: 2388
Sample size of model......: 620
Missing data %............: 74.03685
- processing CollagenPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
SmokerCurrent, family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] SmokerCurrentyes
1.1691 -0.2488 0.5069
Degrees of Freedom: 621 Total (i.e. Null); 619 Residual
Null Deviance: 643
Residual Deviance: 631.9 AIC: 637.9
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + LDL_final, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.2145 0.3945 0.6231 0.7404 1.0366
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.397e+01 1.384e+03 0.010 0.9920
currentDF[, PROTEIN] -2.191e-01 1.036e-01 -2.115 0.0344 *
Age 3.352e-03 1.245e-02 0.269 0.7878
Gendermale -9.782e-02 2.254e-01 -0.434 0.6643
Hypertension.compositeyes 7.913e-02 3.052e-01 0.259 0.7954
DiabetesStatusDiabetes 2.197e-01 2.604e-01 0.844 0.3987
SmokerCurrentyes 5.167e-01 2.289e-01 2.257 0.0240 *
Med.Statin.LLDyes 1.046e-01 2.524e-01 0.414 0.6787
Med.all.antiplateletyes 6.134e-02 3.509e-01 0.175 0.8612
GFR_MDRD 5.423e-03 5.436e-03 0.998 0.3185
BMI 3.696e-02 2.912e-02 1.269 0.2045
CAD_history 2.907e-01 2.389e-01 1.217 0.2236
Stroke_history 1.541e-01 2.218e-01 0.695 0.4871
Peripheral.interv 1.196e-01 2.760e-01 0.433 0.6649
stenose50-70% -1.425e+01 1.384e+03 -0.010 0.9918
stenose70-90% -1.513e+01 1.384e+03 -0.011 0.9913
stenose90-99% -1.512e+01 1.384e+03 -0.011 0.9913
stenose100% (Occlusion) 3.704e-01 1.647e+03 0.000 0.9998
stenose70-99% 3.154e-01 2.190e+03 0.000 0.9999
LDL_final 1.246e-01 1.046e-01 1.192 0.2332
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 643.0 on 621 degrees of freedom
Residual deviance: 617.3 on 602 degrees of freedom
AIC: 657.3
Number of Fisher Scoring iterations: 15
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_rank ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_pg_ug_2015_rank
Trait/outcome.............: CollagenPlaque
Effect size...............: -0.219079
Standard error............: 0.103595
Odds ratio (effect size)..: 0.803
Lower 95% CI..............: 0.656
Upper 95% CI..............: 0.984
Z-value...................: -2.114772
P-value...................: 0.03444938
Hosmer and Lemeshow r^2...: 0.039977
Cox and Snell r^2.........: 0.040484
Nagelkerke's pseudo r^2...: 0.062831
Sample size of AE DB......: 2388
Sample size of model......: 622
Missing data %............: 73.9531
- processing Fat10Perc
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Gender + Stroke_history + Peripheral.interv + LDL_final,
family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Gendermale Stroke_history Peripheral.interv LDL_final
-0.0551 0.4691 0.9566 0.3535 -0.5271 0.1711
Degrees of Freedom: 621 Total (i.e. Null); 616 Residual
Null Deviance: 717.6
Residual Deviance: 660 AIC: 672
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + LDL_final, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.3823 -0.9479 0.5924 0.7833 1.4736
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 12.858469 499.149839 0.026 0.9794
currentDF[, PROTEIN] 0.467195 0.101826 4.588 4.47e-06 ***
Age 0.003838 0.011769 0.326 0.7444
Gendermale 0.981790 0.208027 4.720 2.36e-06 ***
Hypertension.compositeyes 0.165307 0.297749 0.555 0.5788
DiabetesStatusDiabetes -0.102798 0.239740 -0.429 0.6681
SmokerCurrentyes 0.152551 0.213444 0.715 0.4748
Med.Statin.LLDyes -0.279226 0.256107 -1.090 0.2756
Med.all.antiplateletyes 0.210611 0.337766 0.624 0.5329
GFR_MDRD -0.004117 0.005317 -0.774 0.4388
BMI 0.014587 0.025497 0.572 0.5672
CAD_history 0.150945 0.226514 0.666 0.5052
Stroke_history 0.353781 0.221467 1.597 0.1102
Peripheral.interv -0.579450 0.239612 -2.418 0.0156 *
stenose50-70% -13.716620 499.148035 -0.027 0.9781
stenose70-90% -13.360306 499.147872 -0.027 0.9786
stenose90-99% -13.401952 499.147867 -0.027 0.9786
stenose100% (Occlusion) -14.231873 499.148478 -0.029 0.9773
stenose70-99% -14.409382 499.149988 -0.029 0.9770
LDL_final 0.152308 0.101184 1.505 0.1323
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 717.61 on 621 degrees of freedom
Residual deviance: 651.64 on 602 degrees of freedom
AIC: 691.64
Number of Fisher Scoring iterations: 13
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_rank ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_pg_ug_2015_rank
Trait/outcome.............: Fat10Perc
Effect size...............: 0.467195
Standard error............: 0.101826
Odds ratio (effect size)..: 1.596
Lower 95% CI..............: 1.307
Upper 95% CI..............: 1.948
Z-value...................: 4.588153
P-value...................: 4.471852e-06
Hosmer and Lemeshow r^2...: 0.091923
Cox and Snell r^2.........: 0.100623
Nagelkerke's pseudo r^2...: 0.146995
Sample size of AE DB......: 2388
Sample size of model......: 622
Missing data %............: 73.9531
- processing IPH
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + Gender +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + CAD_history +
Stroke_history + Peripheral.interv, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) Age Gendermale SmokerCurrentyes Med.Statin.LLDyes
-1.75978 0.01881 0.57921 0.34166 -0.34769
Med.all.antiplateletyes CAD_history Stroke_history Peripheral.interv
0.61066 0.29144 0.30926 0.34737
Degrees of Freedom: 620 Total (i.e. Null); 612 Residual
Null Deviance: 814.3
Residual Deviance: 786.5 AIC: 804.5
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + LDL_final, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.0242 -1.2532 0.7743 0.9517 1.3858
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -2.479246 1.769091 -1.401 0.1611
currentDF[, PROTEIN] 0.073565 0.088029 0.836 0.4033
Age 0.019453 0.010570 1.840 0.0657 .
Gendermale 0.616088 0.188818 3.263 0.0011 **
Hypertension.compositeyes 0.100506 0.262263 0.383 0.7016
DiabetesStatusDiabetes -0.108476 0.214973 -0.505 0.6138
SmokerCurrentyes 0.334411 0.190624 1.754 0.0794 .
Med.Statin.LLDyes -0.276525 0.223388 -1.238 0.2158
Med.all.antiplateletyes 0.602682 0.296079 2.036 0.0418 *
GFR_MDRD -0.002012 0.004711 -0.427 0.6693
BMI 0.013881 0.023105 0.601 0.5480
CAD_history 0.290876 0.203374 1.430 0.1526
Stroke_history 0.315478 0.192242 1.641 0.1008
Peripheral.interv 0.335048 0.234753 1.427 0.1535
stenose50-70% -0.140654 1.303328 -0.108 0.9141
stenose70-90% 0.056986 1.248890 0.046 0.9636
stenose90-99% 0.165129 1.247312 0.132 0.8947
stenose100% (Occlusion) 0.008095 1.474009 0.005 0.9956
stenose70-99% -0.823543 1.907537 -0.432 0.6659
LDL_final 0.078241 0.088311 0.886 0.3756
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 814.31 on 620 degrees of freedom
Residual deviance: 782.57 on 601 degrees of freedom
AIC: 822.57
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_rank ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_pg_ug_2015_rank
Trait/outcome.............: IPH
Effect size...............: 0.073565
Standard error............: 0.088029
Odds ratio (effect size)..: 1.076
Lower 95% CI..............: 0.906
Upper 95% CI..............: 1.279
Z-value...................: 0.835693
P-value...................: 0.4033275
Hosmer and Lemeshow r^2...: 0.038982
Cox and Snell r^2.........: 0.049832
Nagelkerke's pseudo r^2...: 0.068213
Sample size of AE DB......: 2388
Sample size of model......: 621
Missing data %............: 73.99497
Analysis of IL6R_pg_ug_2015_rank.
- processing CalcificationPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + SmokerCurrent +
Peripheral.interv + stenose + LDL_final, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) Age SmokerCurrentyes Peripheral.interv stenose50-70%
13.3099 0.0214 0.3256 -0.3539 -15.0020
stenose70-90% stenose90-99% stenose100% (Occlusion) stenose70-99% LDL_final
-14.3302 -14.2143 -13.5827 -29.4019 -0.1460
Degrees of Freedom: 613 Total (i.e. Null); 604 Residual
Null Deviance: 849.3
Residual Deviance: 826.6 AIC: 846.6
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + LDL_final, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.6687 -1.1942 0.8591 1.1070 1.6325
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 12.728624 623.837081 0.020 0.9837
currentDF[, PROTEIN] -0.100966 0.086498 -1.167 0.2431
Age 0.023322 0.010485 2.224 0.0261 *
Gendermale 0.008490 0.184910 0.046 0.9634
Hypertension.compositeyes 0.227501 0.252033 0.903 0.3667
DiabetesStatusDiabetes -0.170953 0.211950 -0.807 0.4199
SmokerCurrentyes 0.358610 0.183428 1.955 0.0506 .
Med.Statin.LLDyes -0.206499 0.214384 -0.963 0.3354
Med.all.antiplateletyes 0.124408 0.297736 0.418 0.6761
GFR_MDRD 0.003341 0.004657 0.717 0.4731
BMI 0.008807 0.023091 0.381 0.7029
CAD_history 0.004566 0.193360 0.024 0.9812
Stroke_history -0.185775 0.181611 -1.023 0.3063
Peripheral.interv -0.354190 0.221874 -1.596 0.1104
stenose50-70% -15.051615 623.836044 -0.024 0.9808
stenose70-90% -14.308766 623.835926 -0.023 0.9817
stenose90-99% -14.199931 623.835923 -0.023 0.9818
stenose100% (Occlusion) -13.640920 623.836514 -0.022 0.9826
stenose70-99% -29.520435 870.648387 -0.034 0.9730
LDL_final -0.179057 0.087640 -2.043 0.0410 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 849.30 on 613 degrees of freedom
Residual deviance: 820.65 on 594 degrees of freedom
AIC: 860.65
Number of Fisher Scoring iterations: 13
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_rank ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6R_pg_ug_2015_rank
Trait/outcome.............: CalcificationPlaque
Effect size...............: -0.100966
Standard error............: 0.086498
Odds ratio (effect size)..: 0.904
Lower 95% CI..............: 0.763
Upper 95% CI..............: 1.071
Z-value...................: -1.167258
P-value...................: 0.243106
Hosmer and Lemeshow r^2...: 0.033731
Cox and Snell r^2.........: 0.045586
Nagelkerke's pseudo r^2...: 0.060843
Sample size of AE DB......: 2388
Sample size of model......: 614
Missing data %............: 74.28811
- processing CollagenPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ SmokerCurrent,
family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) SmokerCurrentyes
1.1229 0.4918
Degrees of Freedom: 615 Total (i.e. Null); 614 Residual
Null Deviance: 642.7
Residual Deviance: 637.2 AIC: 641.2
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + LDL_final, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.1875 0.4255 0.6428 0.7528 1.0398
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.357e+01 1.696e+03 0.008 0.9936
currentDF[, PROTEIN] 4.242e-02 1.036e-01 0.410 0.6821
Age 5.963e-03 1.253e-02 0.476 0.6342
Gendermale -2.572e-03 2.218e-01 -0.012 0.9907
Hypertension.compositeyes -9.392e-04 3.017e-01 -0.003 0.9975
DiabetesStatusDiabetes 3.018e-01 2.631e-01 1.147 0.2513
SmokerCurrentyes 5.109e-01 2.287e-01 2.234 0.0255 *
Med.Statin.LLDyes 1.612e-01 2.533e-01 0.637 0.5244
Med.all.antiplateletyes 2.178e-01 3.457e-01 0.630 0.5287
GFR_MDRD 3.951e-03 5.534e-03 0.714 0.4753
BMI 3.821e-02 2.896e-02 1.319 0.1870
CAD_history 2.479e-01 2.368e-01 1.047 0.2951
Stroke_history 8.299e-02 2.202e-01 0.377 0.7063
Peripheral.interv 1.318e-01 2.762e-01 0.477 0.6331
stenose50-70% -1.413e+01 1.696e+03 -0.008 0.9934
stenose70-90% -1.508e+01 1.696e+03 -0.009 0.9929
stenose90-99% -1.498e+01 1.696e+03 -0.009 0.9930
stenose100% (Occlusion) 5.714e-01 1.918e+03 0.000 0.9998
stenose70-99% 6.645e-01 2.399e+03 0.000 0.9998
LDL_final 1.168e-01 1.055e-01 1.107 0.2683
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 642.71 on 615 degrees of freedom
Residual deviance: 622.38 on 596 degrees of freedom
AIC: 662.38
Number of Fisher Scoring iterations: 15
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_rank ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6R_pg_ug_2015_rank
Trait/outcome.............: CollagenPlaque
Effect size...............: 0.042418
Standard error............: 0.103555
Odds ratio (effect size)..: 1.043
Lower 95% CI..............: 0.852
Upper 95% CI..............: 1.278
Z-value...................: 0.409621
P-value...................: 0.6820839
Hosmer and Lemeshow r^2...: 0.031636
Cox and Snell r^2.........: 0.032469
Nagelkerke's pseudo r^2...: 0.050127
Sample size of AE DB......: 2388
Sample size of model......: 616
Missing data %............: 74.20436
- processing Fat10Perc
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Gender + Stroke_history +
Peripheral.interv + LDL_final, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) Gendermale Stroke_history Peripheral.interv LDL_final
-0.1296 0.9069 0.4893 -0.5359 0.1945
Degrees of Freedom: 615 Total (i.e. Null); 611 Residual
Null Deviance: 705.7
Residual Deviance: 669.9 AIC: 679.9
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + LDL_final, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.2447 -1.0798 0.6262 0.7779 1.3515
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 13.310670 623.678659 0.021 0.9830
currentDF[, PROTEIN] 0.034199 0.099179 0.345 0.7302
Age 0.002162 0.012062 0.179 0.8578
Gendermale 0.945942 0.205147 4.611 4.01e-06 ***
Hypertension.compositeyes 0.185795 0.287996 0.645 0.5188
DiabetesStatusDiabetes -0.050143 0.241878 -0.207 0.8358
SmokerCurrentyes 0.200981 0.211717 0.949 0.3425
Med.Statin.LLDyes -0.259883 0.259751 -1.001 0.3171
Med.all.antiplateletyes 0.190832 0.342756 0.557 0.5777
GFR_MDRD -0.002988 0.005464 -0.547 0.5845
BMI -0.009114 0.026181 -0.348 0.7278
CAD_history 0.043912 0.222949 0.197 0.8439
Stroke_history 0.471988 0.220383 2.142 0.0322 *
Peripheral.interv -0.587090 0.239216 -2.454 0.0141 *
stenose50-70% -13.713838 623.677282 -0.022 0.9825
stenose70-90% -13.172205 623.677152 -0.021 0.9831
stenose90-99% -13.285582 623.677146 -0.021 0.9830
stenose100% (Occlusion) -14.098075 623.677650 -0.023 0.9820
stenose70-99% -14.625391 623.678836 -0.023 0.9813
LDL_final 0.163890 0.102288 1.602 0.1091
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 705.67 on 615 degrees of freedom
Residual deviance: 662.35 on 596 degrees of freedom
AIC: 702.35
Number of Fisher Scoring iterations: 13
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_rank ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6R_pg_ug_2015_rank
Trait/outcome.............: Fat10Perc
Effect size...............: 0.034199
Standard error............: 0.099179
Odds ratio (effect size)..: 1.035
Lower 95% CI..............: 0.852
Upper 95% CI..............: 1.257
Z-value...................: 0.344821
P-value...................: 0.7302291
Hosmer and Lemeshow r^2...: 0.061387
Cox and Snell r^2.........: 0.067907
Nagelkerke's pseudo r^2...: 0.099577
Sample size of AE DB......: 2388
Sample size of model......: 616
Missing data %............: 74.20436
- processing IPH
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Med.all.antiplatelet + Stroke_history + Peripheral.interv +
LDL_final, family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Age Gendermale Med.all.antiplateletyes
-2.07785 0.13314 0.01767 0.65321 0.48369
Stroke_history Peripheral.interv LDL_final
0.29070 0.40758 0.12410
Degrees of Freedom: 614 Total (i.e. Null); 607 Residual
Null Deviance: 809.9
Residual Deviance: 785.9 AIC: 801.9
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + LDL_final, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.0019 -1.2521 0.7763 0.9615 1.3905
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -2.1641077 1.9053663 -1.136 0.256042
currentDF[, PROTEIN] 0.1088200 0.0895386 1.215 0.224236
Age 0.0163295 0.0108111 1.510 0.130932
Gendermale 0.6517166 0.1888045 3.452 0.000557 ***
Hypertension.compositeyes 0.1030330 0.2576892 0.400 0.689279
DiabetesStatusDiabetes -0.1919952 0.2164323 -0.887 0.375030
SmokerCurrentyes 0.2857827 0.1913494 1.494 0.135303
Med.Statin.LLDyes -0.2228589 0.2268089 -0.983 0.325812
Med.all.antiplateletyes 0.5088318 0.3038315 1.675 0.093990 .
GFR_MDRD -0.0037458 0.0048722 -0.769 0.442005
BMI -0.0006517 0.0236286 -0.028 0.977995
CAD_history 0.2608588 0.2031769 1.284 0.199177
Stroke_history 0.3035600 0.1922447 1.579 0.114329
Peripheral.interv 0.3490620 0.2385831 1.463 0.143450
stenose50-70% 0.3834730 1.4877947 0.258 0.796603
stenose70-90% 0.4822793 1.4379089 0.335 0.737321
stenose90-99% 0.5349906 1.4365213 0.372 0.709579
stenose100% (Occlusion) 0.4214479 1.6385851 0.257 0.797023
stenose70-99% -0.3476959 2.0349403 -0.171 0.864332
LDL_final 0.0870989 0.0901723 0.966 0.334086
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 809.93 on 614 degrees of freedom
Residual deviance: 779.36 on 595 degrees of freedom
AIC: 819.36
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_rank ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6R_pg_ug_2015_rank
Trait/outcome.............: IPH
Effect size...............: 0.10882
Standard error............: 0.089539
Odds ratio (effect size)..: 1.115
Lower 95% CI..............: 0.935
Upper 95% CI..............: 1.329
Z-value...................: 1.215341
P-value...................: 0.2242361
Hosmer and Lemeshow r^2...: 0.037739
Cox and Snell r^2.........: 0.048486
Nagelkerke's pseudo r^2...: 0.066233
Sample size of AE DB......: 2388
Sample size of model......: 615
Missing data %............: 74.24623
Analysis of MCP1_pg_ug_2015_rank.
- processing CalcificationPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + SmokerCurrent + Peripheral.interv + LDL_final, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Age SmokerCurrentyes Peripheral.interv LDL_final
-1.34079 -0.39792 0.02462 0.40432 -0.30868 -0.11552
Degrees of Freedom: 635 Total (i.e. Null); 630 Residual
Null Deviance: 880.3
Residual Deviance: 840 AIC: 852
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + LDL_final, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.8218 -1.1434 0.7217 1.0746 1.8206
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -1.817023 1.746256 -1.041 0.29810
currentDF[, PROTEIN] -0.399085 0.084476 -4.724 2.31e-06 ***
Age 0.028293 0.010412 2.717 0.00658 **
Gendermale 0.078341 0.185488 0.422 0.67277
Hypertension.compositeyes 0.161837 0.253933 0.637 0.52392
DiabetesStatusDiabetes -0.110912 0.210609 -0.527 0.59845
SmokerCurrentyes 0.436300 0.184027 2.371 0.01775 *
Med.Statin.LLDyes -0.262370 0.212952 -1.232 0.21793
Med.all.antiplateletyes 0.181594 0.292060 0.622 0.53409
GFR_MDRD 0.003821 0.004546 0.840 0.40070
BMI 0.013442 0.022487 0.598 0.55000
CAD_history 0.039467 0.193046 0.204 0.83801
Stroke_history -0.130531 0.182804 -0.714 0.47520
Peripheral.interv -0.358151 0.221203 -1.619 0.10542
stenose50-70% -1.278641 1.309167 -0.977 0.32873
stenose70-90% -0.382827 1.248198 -0.307 0.75907
stenose90-99% -0.361105 1.246825 -0.290 0.77211
stenose100% (Occlusion) 0.094999 1.515418 0.063 0.95001
stenose70-99% -15.461099 595.304646 -0.026 0.97928
LDL_final -0.149867 0.087204 -1.719 0.08569 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 880.27 on 635 degrees of freedom
Residual deviance: 826.32 on 616 degrees of freedom
AIC: 866.32
Number of Fisher Scoring iterations: 13
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: CalcificationPlaque
Effect size...............: -0.399085
Standard error............: 0.084476
Odds ratio (effect size)..: 0.671
Lower 95% CI..............: 0.569
Upper 95% CI..............: 0.792
Z-value...................: -4.724228
P-value...................: 2.309916e-06
Hosmer and Lemeshow r^2...: 0.061283
Cox and Snell r^2.........: 0.081322
Nagelkerke's pseudo r^2...: 0.108509
Sample size of AE DB......: 2388
Sample size of model......: 636
Missing data %............: 73.36683
- processing CollagenPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
SmokerCurrent, family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] SmokerCurrentyes
1.1665 -0.2621 0.5075
Degrees of Freedom: 637 Total (i.e. Null); 635 Residual
Null Deviance: 658.5
Residual Deviance: 644.7 AIC: 650.7
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + LDL_final, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.2941 0.3843 0.6168 0.7386 1.1162
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.370e+01 1.384e+03 0.010 0.99210
currentDF[, PROTEIN] -2.590e-01 9.751e-02 -2.657 0.00789 **
Age 4.463e-03 1.241e-02 0.360 0.71918
Gendermale -1.234e-02 2.222e-01 -0.056 0.95570
Hypertension.compositeyes -4.807e-02 3.030e-01 -0.159 0.87395
DiabetesStatusDiabetes 2.668e-01 2.602e-01 1.025 0.30517
SmokerCurrentyes 5.095e-01 2.285e-01 2.230 0.02577 *
Med.Statin.LLDyes 1.431e-01 2.509e-01 0.570 0.56847
Med.all.antiplateletyes 8.843e-02 3.421e-01 0.259 0.79601
GFR_MDRD 4.884e-03 5.411e-03 0.903 0.36673
BMI 4.008e-02 2.882e-02 1.391 0.16431
CAD_history 2.587e-01 2.349e-01 1.101 0.27074
Stroke_history 1.059e-01 2.196e-01 0.482 0.62957
Peripheral.interv 1.538e-01 2.756e-01 0.558 0.57682
stenose50-70% -1.421e+01 1.384e+03 -0.010 0.99181
stenose70-90% -1.508e+01 1.384e+03 -0.011 0.99130
stenose90-99% -1.502e+01 1.384e+03 -0.011 0.99134
stenose100% (Occlusion) 2.773e-01 1.651e+03 0.000 0.99987
stenose70-99% 6.816e-01 2.189e+03 0.000 0.99975
LDL_final 1.548e-01 1.051e-01 1.473 0.14083
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 658.50 on 637 degrees of freedom
Residual deviance: 629.52 on 618 degrees of freedom
AIC: 669.52
Number of Fisher Scoring iterations: 15
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: CollagenPlaque
Effect size...............: -0.259041
Standard error............: 0.097506
Odds ratio (effect size)..: 0.772
Lower 95% CI..............: 0.638
Upper 95% CI..............: 0.934
Z-value...................: -2.656673
P-value...................: 0.007891602
Hosmer and Lemeshow r^2...: 0.044018
Cox and Snell r^2.........: 0.044416
Nagelkerke's pseudo r^2...: 0.068995
Sample size of AE DB......: 2388
Sample size of model......: 638
Missing data %............: 73.28308
- processing Fat10Perc
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Gender + Stroke_history +
Peripheral.interv + LDL_final, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) Gendermale Stroke_history Peripheral.interv LDL_final
-0.1173 0.9603 0.4530 -0.4809 0.1684
Degrees of Freedom: 637 Total (i.e. Null); 633 Residual
Null Deviance: 737.7
Residual Deviance: 700.3 AIC: 710.3
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + LDL_final, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.2911 -1.0945 0.6282 0.7934 1.3752
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.338e+01 5.031e+02 0.027 0.9788
currentDF[, PROTEIN] 1.202e-01 9.253e-02 1.299 0.1940
Age 1.348e-03 1.157e-02 0.117 0.9072
Gendermale 9.977e-01 2.005e-01 4.976 6.5e-07 ***
Hypertension.compositeyes 2.073e-01 2.844e-01 0.729 0.4660
DiabetesStatusDiabetes -1.147e-01 2.332e-01 -0.492 0.6227
SmokerCurrentyes 2.294e-01 2.070e-01 1.108 0.2678
Med.Statin.LLDyes -2.872e-01 2.521e-01 -1.139 0.2546
Med.all.antiplateletyes 2.759e-01 3.253e-01 0.848 0.3965
GFR_MDRD -5.717e-03 5.177e-03 -1.104 0.2695
BMI 8.212e-04 2.472e-02 0.033 0.9735
CAD_history 4.738e-02 2.173e-01 0.218 0.8274
Stroke_history 4.034e-01 2.162e-01 1.866 0.0620 .
Peripheral.interv -5.442e-01 2.343e-01 -2.323 0.0202 *
stenose50-70% -1.370e+01 5.031e+02 -0.027 0.9783
stenose70-90% -1.326e+01 5.031e+02 -0.026 0.9790
stenose90-99% -1.340e+01 5.031e+02 -0.027 0.9788
stenose100% (Occlusion) -1.410e+01 5.031e+02 -0.028 0.9776
stenose70-99% -1.479e+01 5.031e+02 -0.029 0.9765
LDL_final 1.304e-01 9.848e-02 1.324 0.1856
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 737.67 on 637 degrees of freedom
Residual deviance: 688.83 on 618 degrees of freedom
AIC: 728.83
Number of Fisher Scoring iterations: 13
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: Fat10Perc
Effect size...............: 0.120194
Standard error............: 0.092531
Odds ratio (effect size)..: 1.128
Lower 95% CI..............: 0.941
Upper 95% CI..............: 1.352
Z-value...................: 1.298962
P-value...................: 0.193957
Hosmer and Lemeshow r^2...: 0.0662
Cox and Snell r^2.........: 0.073686
Nagelkerke's pseudo r^2...: 0.10752
Sample size of AE DB......: 2388
Sample size of model......: 638
Missing data %............: 73.28308
- processing IPH
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet +
CAD_history + Stroke_history + Peripheral.interv, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Age Gendermale SmokerCurrentyes
-1.67915 -0.12054 0.01775 0.61982 0.34171
Med.Statin.LLDyes Med.all.antiplateletyes CAD_history Stroke_history Peripheral.interv
-0.43459 0.59803 0.30304 0.37355 0.40386
Degrees of Freedom: 636 Total (i.e. Null); 627 Residual
Null Deviance: 837.7
Residual Deviance: 804.1 AIC: 824.1
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + LDL_final, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.9427 -1.2478 0.7625 0.9458 1.5222
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -2.127639 1.772751 -1.200 0.23007
currentDF[, PROTEIN] -0.131427 0.083463 -1.575 0.11533
Age 0.017852 0.010561 1.690 0.09095 .
Gendermale 0.657838 0.186851 3.521 0.00043 ***
Hypertension.compositeyes 0.037915 0.257418 0.147 0.88290
DiabetesStatusDiabetes -0.150861 0.212631 -0.709 0.47802
SmokerCurrentyes 0.335587 0.188712 1.778 0.07535 .
Med.Statin.LLDyes -0.375382 0.224150 -1.675 0.09399 .
Med.all.antiplateletyes 0.592618 0.292041 2.029 0.04244 *
GFR_MDRD -0.002568 0.004689 -0.548 0.58388
BMI 0.007938 0.022724 0.349 0.72684
CAD_history 0.304739 0.199481 1.528 0.12660
Stroke_history 0.391671 0.191529 2.045 0.04086 *
Peripheral.interv 0.399298 0.234514 1.703 0.08863 .
stenose50-70% -0.123512 1.318453 -0.094 0.92536
stenose70-90% 0.121608 1.264382 0.096 0.92338
stenose90-99% 0.190347 1.262909 0.151 0.88020
stenose100% (Occlusion) -0.048944 1.488937 -0.033 0.97378
stenose70-99% -0.908097 1.915050 -0.474 0.63536
LDL_final 0.073318 0.088201 0.831 0.40583
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 837.69 on 636 degrees of freedom
Residual deviance: 801.03 on 617 degrees of freedom
AIC: 841.03
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: IPH
Effect size...............: -0.131427
Standard error............: 0.083463
Odds ratio (effect size)..: 0.877
Lower 95% CI..............: 0.745
Upper 95% CI..............: 1.033
Z-value...................: -1.574674
P-value...................: 0.1153318
Hosmer and Lemeshow r^2...: 0.043766
Cox and Snell r^2.........: 0.05593
Nagelkerke's pseudo r^2...: 0.076455
Sample size of AE DB......: 2388
Sample size of model......: 637
Missing data %............: 73.32496 cat("Edit the column names...\n")Edit the column names...colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
"Beta", "s.e.m.",
"OR", "low95CI", "up95CI",
"Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")
cat("Correct the variable types...\n")Correct the variable types...GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)
# Save the data
cat("Writing results to Excel-file...\n")Writing results to Excel-file...### Univariate
write.xlsx(GLM.results,
file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Multi.Protein.PlaquePhenotypes.RANK.MODEL3.xlsx"),
row.names = FALSE, col.names = TRUE, sheetName = "Bin.Multi.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")Removing intermediate files...rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)
Model 4
In this model we correct for Age, Gender, Hypertension status, Diabetes status, current smoker status, lipid-lowering drugs (LLDs), antiplatelet medication, eGFR (MDRD), BMI, CAD history, stroke history, peripheral interventions, stenosis, and hsCRP.
Natural log-transformed data
First we use the natural-log transformed data.
Analysis of continuous/quantitative plaque traits as a function of serum/plaque IL6(R)/MCP1 levels.
GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")Running linear regression...for (protein in 1:length(TRAITS.PROTEIN)) {
PROTEIN = TRAITS.PROTEIN[protein]
cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
for (trait in 1:length(TRAITS.CON)) {
TRAIT = TRAITS.CON[trait]
cat(paste0("\n- processing ",TRAIT,"\n\n"))
currentDF <- as.data.frame(AEDB.CEA %>%
dplyr::select(., PROTEIN, TRAIT, COVARIATES_M4) %>%
filter(complete.cases(.))) %>%
filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
# for debug
# print(DT::datatable(currentDF))
# print(nrow(currentDF))
# print(str(currentDF))
### univariate
fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender +
Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose + hsCRP_plasma,
data = currentDF)
model_step <- stepAIC(fit, direction = "both", trace = FALSE)
print(model_step)
print(summary(fit))
GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
GLM.results.TEMP[1,] = GLM.CON(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
GLM.results = rbind(GLM.results, GLM.results.TEMP)
}
}
Analysis of IL6_LN.
- processing Macrophages_LNNote: Using an external vector in selections is ambiguous.
[34mℹ[39m Use `all_of(COVARIATES_M4)` instead of `COVARIATES_M4` to silence this message.
[34mℹ[39m See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
[90mThis message is displayed once per session.[39m
Call:
lm(formula = currentDF[, PROTEIN] ~ DiabetesStatus + GFR_MDRD +
CAD_history + hsCRP_plasma, data = currentDF)
Coefficients:
(Intercept) DiabetesStatusDiabetes GFR_MDRD CAD_history hsCRP_plasma
3.332070 -0.355918 0.008097 0.225954 0.002694
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose +
hsCRP_plasma, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.8586 -0.7513 -0.0263 0.6686 3.7591
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.154970 1.598166 4.477 1.27e-05 ***
currentDF[, TRAIT] -0.042782 0.036352 -1.177 0.2406
Age -0.008560 0.010390 -0.824 0.4110
Gendermale -0.137008 0.170390 -0.804 0.4223
Hypertension.compositeyes -0.077102 0.218758 -0.352 0.7249
DiabetesStatusDiabetes -0.347297 0.210691 -1.648 0.1008
SmokerCurrentyes -0.008344 0.158557 -0.053 0.9581
Med.Statin.LLDyes -0.081304 0.160921 -0.505 0.6139
Med.all.antiplateletyes -0.161602 0.287511 -0.562 0.5747
GFR_MDRD 0.007092 0.004574 1.551 0.1226
BMI -0.007852 0.019866 -0.395 0.6931
CAD_history 0.307826 0.162579 1.893 0.0597 .
Stroke_history -0.083226 0.160434 -0.519 0.6045
Peripheral.interv 0.072404 0.180065 0.402 0.6880
stenose50-70% -2.941763 1.216350 -2.419 0.0165 *
stenose70-90% -2.647244 1.081787 -2.447 0.0153 *
stenose90-99% -2.691171 1.078445 -2.495 0.0134 *
stenose100% (Occlusion) -3.243744 1.269621 -2.555 0.0114 *
hsCRP_plasma 0.002608 0.001766 1.477 0.1413
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.046 on 201 degrees of freedom
Multiple R-squared: 0.09531, Adjusted R-squared: 0.0143
F-statistic: 1.176 on 18 and 201 DF, p-value: 0.2834
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_LN ' with ' Macrophages_LN ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_LN
Trait/outcome.............: Macrophages_LN
Effect size...............: -0.042782
Standard error............: 0.036352
Odds ratio (effect size)..: 0.958
Lower 95% CI..............: 0.892
Upper 95% CI..............: 1.029
T-value...................: -1.176897
P-value...................: 0.2406286
R^2.......................: 0.095313
Adjusted r^2..............: 0.014297
Sample size of AE DB......: 2388
Sample size of model......: 220
Missing data %............: 90.78727
- processing SMC_LN
Call:
lm(formula = currentDF[, PROTEIN] ~ DiabetesStatus + GFR_MDRD +
Peripheral.interv, data = currentDF)
Coefficients:
(Intercept) DiabetesStatusDiabetes GFR_MDRD Peripheral.interv
3.513131 -0.349405 0.006456 0.283408
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose +
hsCRP_plasma, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.0429 -0.7102 -0.0068 0.7129 3.6927
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.5280157 1.5680212 4.801 3.02e-06 ***
currentDF[, TRAIT] -0.0582733 0.0534464 -1.090 0.2768
Age -0.0115157 0.0099316 -1.159 0.2476
Gendermale -0.1584798 0.1660816 -0.954 0.3411
Hypertension.compositeyes -0.1237622 0.2072961 -0.597 0.5511
DiabetesStatusDiabetes -0.3232686 0.2061713 -1.568 0.1184
SmokerCurrentyes -0.0725585 0.1555184 -0.467 0.6413
Med.Statin.LLDyes -0.1180983 0.1583704 -0.746 0.4567
Med.all.antiplateletyes -0.1487709 0.2863161 -0.520 0.6039
GFR_MDRD 0.0063622 0.0045939 1.385 0.1676
BMI -0.0154651 0.0190781 -0.811 0.4185
CAD_history 0.2714892 0.1624776 1.671 0.0962 .
Stroke_history -0.0335464 0.1577675 -0.213 0.8318
Peripheral.interv 0.1904502 0.1814237 1.050 0.2951
stenose50-70% -2.6864090 1.2205761 -2.201 0.0288 *
stenose70-90% -2.3757456 1.0827885 -2.194 0.0293 *
stenose90-99% -2.4339008 1.0785472 -2.257 0.0251 *
stenose100% (Occlusion) -3.0893634 1.2658256 -2.441 0.0155 *
hsCRP_plasma 0.0003386 0.0007429 0.456 0.6491
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.047 on 207 degrees of freedom
Multiple R-squared: 0.08992, Adjusted R-squared: 0.01078
F-statistic: 1.136 on 18 and 207 DF, p-value: 0.3191
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_LN ' with ' SMC_LN ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_LN
Trait/outcome.............: SMC_LN
Effect size...............: -0.058273
Standard error............: 0.053446
Odds ratio (effect size)..: 0.943
Lower 95% CI..............: 0.85
Upper 95% CI..............: 1.048
T-value...................: -1.090312
P-value...................: 0.2768431
R^2.......................: 0.08992
Adjusted r^2..............: 0.010783
Sample size of AE DB......: 2388
Sample size of model......: 226
Missing data %............: 90.53601
- processing VesselDensity_LN
Call:
lm(formula = currentDF[, PROTEIN] ~ DiabetesStatus + GFR_MDRD +
CAD_history, data = currentDF)
Coefficients:
(Intercept) DiabetesStatusDiabetes GFR_MDRD CAD_history
3.31816 -0.33383 0.00915 0.22071
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose +
hsCRP_plasma, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.8724 -0.7374 -0.0104 0.7630 3.5688
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.8007864 1.5987118 4.879 2.16e-06 ***
currentDF[, TRAIT] -0.1517641 0.1088994 -1.394 0.1650
Age -0.0130366 0.0099970 -1.304 0.1937
Gendermale -0.1134130 0.1662476 -0.682 0.4959
Hypertension.compositeyes -0.0308333 0.2126787 -0.145 0.8849
DiabetesStatusDiabetes -0.3325556 0.2078262 -1.600 0.1111
SmokerCurrentyes -0.0433421 0.1550346 -0.280 0.7801
Med.Statin.LLDyes -0.0542840 0.1579050 -0.344 0.7314
Med.all.antiplateletyes -0.0819246 0.2909088 -0.282 0.7785
GFR_MDRD 0.0070069 0.0045972 1.524 0.1290
BMI -0.0124769 0.0189975 -0.657 0.5121
CAD_history 0.2953265 0.1628940 1.813 0.0713 .
Stroke_history -0.0637634 0.1565213 -0.407 0.6842
Peripheral.interv 0.1249545 0.1795735 0.696 0.4873
stenose50-70% -2.9441863 1.2085588 -2.436 0.0157 *
stenose70-90% -2.6033879 1.0757426 -2.420 0.0164 *
stenose90-99% -2.6655527 1.0725854 -2.485 0.0138 *
stenose100% (Occlusion) -2.4618653 1.3467240 -1.828 0.0690 .
hsCRP_plasma 0.0004629 0.0007375 0.628 0.5310
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.038 on 201 degrees of freedom
Multiple R-squared: 0.1014, Adjusted R-squared: 0.02092
F-statistic: 1.26 on 18 and 201 DF, p-value: 0.2177
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_LN ' with ' VesselDensity_LN ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_LN
Trait/outcome.............: VesselDensity_LN
Effect size...............: -0.151764
Standard error............: 0.108899
Odds ratio (effect size)..: 0.859
Lower 95% CI..............: 0.694
Upper 95% CI..............: 1.064
T-value...................: -1.393618
P-value...................: 0.164972
R^2.......................: 0.101392
Adjusted r^2..............: 0.02092
Sample size of AE DB......: 2388
Sample size of model......: 220
Missing data %............: 90.78727
Analysis of MCP1_LN.
- processing Macrophages_LN
Call:
lm(formula = currentDF[, PROTEIN] ~ Age + Gender + Hypertension.composite +
Med.Statin.LLD, data = currentDF)
Coefficients:
(Intercept) Age Gendermale Hypertension.compositeyes Med.Statin.LLDyes
5.55361 -0.01045 0.34756 -0.28582 -0.23751
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose +
hsCRP_plasma, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.2514 -0.5954 0.1000 0.5746 1.8421
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.230223 1.238018 5.840 1.6e-08 ***
currentDF[, TRAIT] 0.023207 0.027834 0.834 0.40521
Age -0.014468 0.007419 -1.950 0.05229 .
Gendermale 0.332433 0.123271 2.697 0.00747 **
Hypertension.compositeyes -0.329740 0.157218 -2.097 0.03696 *
DiabetesStatusDiabetes -0.100619 0.150749 -0.667 0.50509
SmokerCurrentyes -0.097936 0.117929 -0.830 0.40706
Med.Statin.LLDyes -0.245651 0.122705 -2.002 0.04636 *
Med.all.antiplateletyes -0.049214 0.214926 -0.229 0.81907
GFR_MDRD -0.000930 0.003266 -0.285 0.77608
BMI -0.008874 0.014138 -0.628 0.53077
CAD_history 0.130210 0.122869 1.060 0.29027
Stroke_history -0.069389 0.119888 -0.579 0.56325
Peripheral.interv -0.127172 0.136891 -0.929 0.35378
stenose50-70% -1.096950 0.983505 -1.115 0.26576
stenose70-90% -0.885463 0.896243 -0.988 0.32411
stenose90-99% -0.915352 0.895230 -1.022 0.30754
stenose100% (Occlusion) -2.251892 1.040082 -2.165 0.03132 *
hsCRP_plasma -0.000126 0.001446 -0.087 0.93063
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8735 on 252 degrees of freedom
Multiple R-squared: 0.1049, Adjusted R-squared: 0.04097
F-statistic: 1.641 on 18 and 252 DF, p-value: 0.05087
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_LN ' with ' Macrophages_LN ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_LN
Trait/outcome.............: Macrophages_LN
Effect size...............: 0.023206
Standard error............: 0.027834
Odds ratio (effect size)..: 1.023
Lower 95% CI..............: 0.969
Upper 95% CI..............: 1.081
T-value...................: 0.833751
P-value...................: 0.4052109
R^2.......................: 0.104906
Adjusted r^2..............: 0.040971
Sample size of AE DB......: 2388
Sample size of model......: 271
Missing data %............: 88.65159
- processing SMC_LN
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + Med.Statin.LLD + stenose,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age Gendermale Hypertension.compositeyes
6.62249 -0.10860 -0.01512 0.37129 -0.30505
Med.Statin.LLDyes stenose50-70% stenose70-90% stenose90-99% stenose100% (Occlusion)
-0.24448 -0.76334 -0.72115 -0.71934 -2.12656
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose +
hsCRP_plasma, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.10855 -0.58245 0.01592 0.59716 1.91247
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.294e+00 1.196e+00 6.099 3.89e-09 ***
currentDF[, TRAIT] -1.086e-01 3.656e-02 -2.971 0.00325 **
Age -1.758e-02 7.046e-03 -2.496 0.01320 *
Gendermale 3.277e-01 1.187e-01 2.760 0.00620 **
Hypertension.compositeyes -3.428e-01 1.483e-01 -2.312 0.02155 *
DiabetesStatusDiabetes -8.723e-02 1.464e-01 -0.596 0.55176
SmokerCurrentyes -1.007e-01 1.130e-01 -0.891 0.37373
Med.Statin.LLDyes -2.400e-01 1.189e-01 -2.018 0.04460 *
Med.all.antiplateletyes -8.667e-02 2.097e-01 -0.413 0.67969
GFR_MDRD 2.996e-05 3.182e-03 0.009 0.99250
BMI -5.124e-03 1.346e-02 -0.381 0.70382
CAD_history 1.832e-01 1.202e-01 1.524 0.12871
Stroke_history -1.968e-02 1.158e-01 -0.170 0.86514
Peripheral.interv -1.622e-01 1.339e-01 -1.211 0.22711
stenose50-70% -1.037e+00 9.639e-01 -1.076 0.28306
stenose70-90% -9.141e-01 8.774e-01 -1.042 0.29843
stenose90-99% -9.206e-01 8.757e-01 -1.051 0.29414
stenose100% (Occlusion) -2.393e+00 1.016e+00 -2.356 0.01925 *
hsCRP_plasma 1.276e-04 6.039e-04 0.211 0.83279
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8557 on 258 degrees of freedom
Multiple R-squared: 0.1382, Adjusted R-squared: 0.07811
F-statistic: 2.299 on 18 and 258 DF, p-value: 0.002402
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_LN ' with ' SMC_LN ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_LN
Trait/outcome.............: SMC_LN
Effect size...............: -0.108601
Standard error............: 0.036558
Odds ratio (effect size)..: 0.897
Lower 95% CI..............: 0.835
Upper 95% CI..............: 0.964
T-value...................: -2.970624
P-value...................: 0.003252192
R^2.......................: 0.138234
Adjusted r^2..............: 0.07811
Sample size of AE DB......: 2388
Sample size of model......: 277
Missing data %............: 88.40033
- processing VesselDensity_LN
Call:
lm(formula = currentDF[, PROTEIN] ~ Age + Gender + Hypertension.composite +
Med.Statin.LLD + stenose, data = currentDF)
Coefficients:
(Intercept) Age Gendermale Hypertension.compositeyes Med.Statin.LLDyes
6.45526 -0.01313 0.42480 -0.29184 -0.24996
stenose50-70% stenose70-90% stenose90-99% stenose100% (Occlusion)
-0.88050 -0.74244 -0.74446 -2.08812
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose +
hsCRP_plasma, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.2211 -0.5497 0.0680 0.5714 1.9122
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.2769935 1.2463069 5.839 1.61e-08 ***
currentDF[, TRAIT] -0.0485649 0.0770553 -0.630 0.52909
Age -0.0153724 0.0072135 -2.131 0.03405 *
Gendermale 0.3816541 0.1223969 3.118 0.00203 **
Hypertension.compositeyes -0.3179798 0.1523841 -2.087 0.03792 *
DiabetesStatusDiabetes -0.0979577 0.1491193 -0.657 0.51184
SmokerCurrentyes -0.1164997 0.1155608 -1.008 0.31436
Med.Statin.LLDyes -0.2397153 0.1214925 -1.973 0.04957 *
Med.all.antiplateletyes -0.0723110 0.2129965 -0.339 0.73452
GFR_MDRD 0.0002343 0.0032848 0.071 0.94320
BMI -0.0081189 0.0136832 -0.593 0.55348
CAD_history 0.1596758 0.1233051 1.295 0.19651
Stroke_history -0.0527180 0.1178355 -0.447 0.65498
Peripheral.interv -0.1166770 0.1375548 -0.848 0.39712
stenose50-70% -1.1744730 0.9795354 -1.199 0.23165
stenose70-90% -0.9491066 0.8939936 -1.062 0.28941
stenose90-99% -0.9603463 0.8920512 -1.077 0.28270
stenose100% (Occlusion) -2.3642172 1.0347919 -2.285 0.02316 *
hsCRP_plasma 0.0001594 0.0006141 0.259 0.79547
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8689 on 253 degrees of freedom
Multiple R-squared: 0.1154, Adjusted R-squared: 0.05245
F-statistic: 1.833 on 18 and 253 DF, p-value: 0.02208
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_LN ' with ' VesselDensity_LN ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_LN
Trait/outcome.............: VesselDensity_LN
Effect size...............: -0.048565
Standard error............: 0.077055
Odds ratio (effect size)..: 0.953
Lower 95% CI..............: 0.819
Upper 95% CI..............: 1.108
T-value...................: -0.63026
P-value...................: 0.5290932
R^2.......................: 0.115389
Adjusted r^2..............: 0.052452
Sample size of AE DB......: 2388
Sample size of model......: 272
Missing data %............: 88.60971
Analysis of IL6_pg_ug_2015_LN.
- processing Macrophages_LN
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + CAD_history +
Stroke_history, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] CAD_history Stroke_history
-3.0888 0.1093 -0.3774 0.3903
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose +
hsCRP_plasma, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-6.5508 -0.8878 0.0457 0.8449 4.8428
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2.4222537 1.2000578 -2.018 0.044005 *
currentDF[, TRAIT] 0.1088754 0.0316577 3.439 0.000626 ***
Age -0.0089860 0.0074745 -1.202 0.229772
Gendermale 0.0975047 0.1296461 0.752 0.452306
Hypertension.compositeyes -0.0224749 0.1802234 -0.125 0.900800
DiabetesStatusDiabetes 0.0386768 0.1479191 0.261 0.793821
SmokerCurrentyes 0.0852958 0.1298546 0.657 0.511535
Med.Statin.LLDyes -0.1932175 0.1432745 -1.349 0.177998
Med.all.antiplateletyes 0.0843741 0.2046690 0.412 0.680311
GFR_MDRD -0.0032847 0.0032672 -1.005 0.315152
BMI -0.0184962 0.0160116 -1.155 0.248496
CAD_history -0.3320098 0.1363873 -2.434 0.015221 *
Stroke_history 0.3735926 0.1282652 2.913 0.003722 **
Peripheral.interv -0.0317326 0.1514970 -0.209 0.834163
stenose50-70% 0.4969845 0.8633049 0.576 0.565057
stenose70-90% 0.7618668 0.8303871 0.917 0.359271
stenose90-99% 0.5369724 0.8302994 0.647 0.518069
stenose100% (Occlusion) 1.0861297 1.0996247 0.988 0.323699
stenose70-99% 1.4968708 1.1666027 1.283 0.199970
hsCRP_plasma 0.0003995 0.0005636 0.709 0.478709
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.423 on 579 degrees of freedom
Multiple R-squared: 0.07298, Adjusted R-squared: 0.04256
F-statistic: 2.399 on 19 and 579 DF, p-value: 0.0008015
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_LN ' with ' Macrophages_LN ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_pg_ug_2015_LN
Trait/outcome.............: Macrophages_LN
Effect size...............: 0.108875
Standard error............: 0.031658
Odds ratio (effect size)..: 1.115
Lower 95% CI..............: 1.048
Upper 95% CI..............: 1.186
T-value...................: 3.439142
P-value...................: 0.0006255322
R^2.......................: 0.072977
Adjusted r^2..............: 0.042557
Sample size of AE DB......: 2388
Sample size of model......: 599
Missing data %............: 74.91625
- processing SMC_LN
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Med.Statin.LLD + CAD_history + Stroke_history, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age Med.Statin.LLDyes CAD_history Stroke_history
-2.27598 -0.13586 -0.01229 -0.21418 -0.28862 0.41719
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose +
hsCRP_plasma, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-6.3182 -0.8724 0.0207 0.8745 4.5800
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2.5608054 1.1899666 -2.152 0.03181 *
currentDF[, TRAIT] -0.1279810 0.0417212 -3.068 0.00226 **
Age -0.0136454 0.0073632 -1.853 0.06436 .
Gendermale 0.0953330 0.1289844 0.739 0.46014
Hypertension.compositeyes 0.0065689 0.1771091 0.037 0.97043
DiabetesStatusDiabetes 0.0604255 0.1478941 0.409 0.68300
SmokerCurrentyes 0.0614024 0.1288767 0.476 0.63394
Med.Statin.LLDyes -0.2091099 0.1414530 -1.478 0.13986
Med.all.antiplateletyes 0.0606548 0.2050098 0.296 0.76744
GFR_MDRD -0.0023243 0.0032865 -0.707 0.47970
BMI -0.0118293 0.0157425 -0.751 0.45270
CAD_history -0.3029891 0.1361979 -2.225 0.02649 *
Stroke_history 0.3925245 0.1273728 3.082 0.00215 **
Peripheral.interv -0.0479725 0.1512385 -0.317 0.75121
stenose50-70% 0.4905981 0.8624027 0.569 0.56966
stenose70-90% 0.7947610 0.8310592 0.956 0.33930
stenose90-99% 0.6256534 0.8314988 0.752 0.45209
stenose100% (Occlusion) 1.0784576 1.0999095 0.980 0.32725
stenose70-99% 1.6124436 1.1671141 1.382 0.16763
hsCRP_plasma 0.0004751 0.0005016 0.947 0.34395
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.423 on 586 degrees of freedom
Multiple R-squared: 0.0681, Adjusted R-squared: 0.03788
F-statistic: 2.254 on 19 and 586 DF, p-value: 0.001815
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_LN ' with ' SMC_LN ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_pg_ug_2015_LN
Trait/outcome.............: SMC_LN
Effect size...............: -0.127981
Standard error............: 0.041721
Odds ratio (effect size)..: 0.88
Lower 95% CI..............: 0.811
Upper 95% CI..............: 0.955
T-value...................: -3.067533
P-value...................: 0.002257945
R^2.......................: 0.068098
Adjusted r^2..............: 0.037882
Sample size of AE DB......: 2388
Sample size of model......: 606
Missing data %............: 74.62312
- processing VesselDensity_LN
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Med.Statin.LLD +
CAD_history + Stroke_history, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Med.Statin.LLDyes CAD_history Stroke_history
-2.8844 -0.1347 -0.2053 -0.3254 0.4094
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose +
hsCRP_plasma, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-6.4486 -0.8206 0.0114 0.8406 4.5942
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2.1804508 1.2366293 -1.763 0.07844 .
currentDF[, TRAIT] -0.1363934 0.0698575 -1.952 0.05141 .
Age -0.0090416 0.0077252 -1.170 0.24236
Gendermale 0.0851619 0.1349328 0.631 0.52822
Hypertension.compositeyes -0.0401191 0.1873976 -0.214 0.83056
DiabetesStatusDiabetes 0.0529991 0.1572577 0.337 0.73623
SmokerCurrentyes 0.1252375 0.1365104 0.917 0.35934
Med.Statin.LLDyes -0.2091830 0.1489028 -1.405 0.16066
Med.all.antiplateletyes -0.0138050 0.2249842 -0.061 0.95110
GFR_MDRD -0.0025894 0.0035298 -0.734 0.46353
BMI -0.0152657 0.0163816 -0.932 0.35182
CAD_history -0.2779734 0.1444831 -1.924 0.05490 .
Stroke_history 0.3829146 0.1349112 2.838 0.00471 **
Peripheral.interv -0.0375603 0.1617412 -0.232 0.81645
stenose50-70% 0.1874127 0.8794295 0.213 0.83133
stenose70-90% 0.5433789 0.8400991 0.647 0.51804
stenose90-99% 0.3455578 0.8393161 0.412 0.68072
stenose100% (Occlusion) 0.8137303 1.1099752 0.733 0.46382
stenose70-99% 2.9446594 1.6628302 1.771 0.07715 .
hsCRP_plasma 0.0006493 0.0008577 0.757 0.44933
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.433 on 532 degrees of freedom
Multiple R-squared: 0.06303, Adjusted R-squared: 0.02956
F-statistic: 1.883 on 19 and 532 DF, p-value: 0.01328
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_LN ' with ' VesselDensity_LN ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_pg_ug_2015_LN
Trait/outcome.............: VesselDensity_LN
Effect size...............: -0.136393
Standard error............: 0.069857
Odds ratio (effect size)..: 0.872
Lower 95% CI..............: 0.761
Upper 95% CI..............: 1.001
T-value...................: -1.952452
P-value...................: 0.05140865
R^2.......................: 0.063026
Adjusted r^2..............: 0.029562
Sample size of AE DB......: 2388
Sample size of model......: 552
Missing data %............: 76.88442
Analysis of IL6R_pg_ug_2015_LN.
- processing Macrophages_LN
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + DiabetesStatus +
Med.Statin.LLD + CAD_history + Stroke_history + Peripheral.interv +
stenose, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] DiabetesStatusDiabetes Med.Statin.LLDyes CAD_history
-2.72515 0.12169 -0.23102 -0.41274 -0.17988
Stroke_history Peripheral.interv stenose50-70% stenose70-90% stenose90-99%
0.21758 0.32738 1.02141 1.04648 1.31783
stenose100% (Occlusion) stenose50-99% stenose70-99%
0.80210 1.02716 -0.06394
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose +
hsCRP_plasma, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-7.2446 -0.5444 0.1137 0.7057 2.9730
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.5498124 1.0648708 -1.455 0.146097
currentDF[, TRAIT] 0.1242813 0.0252771 4.917 1.15e-06 ***
Age -0.0062752 0.0060075 -1.045 0.296654
Gendermale -0.0379152 0.1036725 -0.366 0.714706
Hypertension.compositeyes -0.0443839 0.1438683 -0.309 0.757809
DiabetesStatusDiabetes -0.2268754 0.1183648 -1.917 0.055758 .
SmokerCurrentyes 0.0655855 0.1041078 0.630 0.528957
Med.Statin.LLDyes -0.4202807 0.1157337 -3.631 0.000307 ***
Med.all.antiplateletyes -0.0478602 0.1645514 -0.291 0.771267
GFR_MDRD -0.0035106 0.0026211 -1.339 0.180975
BMI -0.0173525 0.0132633 -1.308 0.191286
CAD_history -0.1464790 0.1097927 -1.334 0.182678
Stroke_history 0.2072312 0.1027701 2.016 0.044211 *
Peripheral.interv 0.3040732 0.1217287 2.498 0.012765 *
stenose50-70% 1.1008527 0.8402367 1.310 0.190654
stenose70-90% 1.1052473 0.8182193 1.351 0.177285
stenose90-99% 1.3678656 0.8184965 1.671 0.095220 .
stenose100% (Occlusion) 0.7729226 1.0016549 0.772 0.440637
stenose50-99% 1.1115231 1.1502332 0.966 0.334272
stenose70-99% -0.0056577 1.0502892 -0.005 0.995704
hsCRP_plasma -0.0002985 0.0004536 -0.658 0.510846
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.145 on 583 degrees of freedom
Multiple R-squared: 0.1217, Adjusted R-squared: 0.09155
F-statistic: 4.038 on 20 and 583 DF, p-value: 1.365e-08
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_LN ' with ' Macrophages_LN ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6R_pg_ug_2015_LN
Trait/outcome.............: Macrophages_LN
Effect size...............: 0.124281
Standard error............: 0.025277
Odds ratio (effect size)..: 1.132
Lower 95% CI..............: 1.078
Upper 95% CI..............: 1.19
T-value...................: 4.916746
P-value...................: 1.145657e-06
R^2.......................: 0.121682
Adjusted r^2..............: 0.091551
Sample size of AE DB......: 2388
Sample size of model......: 604
Missing data %............: 74.70687
- processing SMC_LN
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + DiabetesStatus +
Med.Statin.LLD + CAD_history + Stroke_history + Peripheral.interv +
stenose, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] DiabetesStatusDiabetes Med.Statin.LLDyes CAD_history
-2.89712 0.05661 -0.22774 -0.41591 -0.17720
Stroke_history Peripheral.interv stenose50-70% stenose70-90% stenose90-99%
0.24162 0.28469 1.00305 1.02737 1.33187
stenose100% (Occlusion) stenose50-99% stenose70-99%
0.71296 0.91858 -0.03186
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose +
hsCRP_plasma, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-7.3697 -0.5251 0.0999 0.6793 3.0657
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.6616722 1.0734979 -1.548 0.122181
currentDF[, TRAIT] 0.0541870 0.0343533 1.577 0.115252
Age -0.0096233 0.0060105 -1.601 0.109891
Gendermale 0.0371287 0.1048229 0.354 0.723313
Hypertension.compositeyes -0.0252319 0.1435207 -0.176 0.860506
DiabetesStatusDiabetes -0.2311323 0.1201514 -1.924 0.054876 .
SmokerCurrentyes 0.0471135 0.1049359 0.449 0.653615
Med.Statin.LLDyes -0.4305494 0.1160480 -3.710 0.000227 ***
Med.all.antiplateletyes -0.0452318 0.1673390 -0.270 0.787023
GFR_MDRD -0.0036355 0.0026762 -1.358 0.174832
BMI -0.0115840 0.0132147 -0.877 0.381061
CAD_history -0.1490517 0.1113280 -1.339 0.181134
Stroke_history 0.2455585 0.1036470 2.369 0.018148 *
Peripheral.interv 0.2647824 0.1233105 2.147 0.032178 *
stenose50-70% 1.0495555 0.8529759 1.230 0.219014
stenose70-90% 1.0503071 0.8317698 1.263 0.207182
stenose90-99% 1.3454201 0.8324336 1.616 0.106575
stenose100% (Occlusion) 0.6316689 1.0182788 0.620 0.535280
stenose50-99% 0.9915459 1.1681242 0.849 0.396317
stenose70-99% 0.0392846 1.0669644 0.037 0.970642
hsCRP_plasma -0.0002766 0.0004100 -0.675 0.500087
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.163 on 590 degrees of freedom
Multiple R-squared: 0.09125, Adjusted R-squared: 0.06045
F-statistic: 2.962 on 20 and 590 DF, p-value: 1.844e-05
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_LN ' with ' SMC_LN ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6R_pg_ug_2015_LN
Trait/outcome.............: SMC_LN
Effect size...............: 0.054187
Standard error............: 0.034353
Odds ratio (effect size)..: 1.056
Lower 95% CI..............: 0.987
Upper 95% CI..............: 1.129
T-value...................: 1.577347
P-value...................: 0.1152517
R^2.......................: 0.091252
Adjusted r^2..............: 0.060446
Sample size of AE DB......: 2388
Sample size of model......: 611
Missing data %............: 74.41374
- processing VesselDensity_LN
Call:
lm(formula = currentDF[, PROTEIN] ~ Age + DiabetesStatus + Med.Statin.LLD +
CAD_history + Stroke_history + Peripheral.interv + stenose,
data = currentDF)
Coefficients:
(Intercept) Age DiabetesStatusDiabetes Med.Statin.LLDyes CAD_history
-2.286903 -0.009033 -0.247772 -0.491583 -0.194727
Stroke_history Peripheral.interv stenose50-70% stenose70-90% stenose90-99%
0.244739 0.278970 1.108588 1.158943 1.433943
stenose100% (Occlusion) stenose50-99% stenose70-99%
0.739330 0.991161 -0.294824
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose +
hsCRP_plasma, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-7.3050 -0.5167 0.0747 0.6748 3.0675
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.627e+00 1.105e+00 -1.472 0.1415
currentDF[, TRAIT] 5.713e-02 5.681e-02 1.006 0.3150
Age -1.151e-02 6.294e-03 -1.828 0.0681 .
Gendermale 8.824e-03 1.092e-01 0.081 0.9356
Hypertension.compositeyes -1.964e-02 1.517e-01 -0.129 0.8970
DiabetesStatusDiabetes -2.573e-01 1.273e-01 -2.021 0.0438 *
SmokerCurrentyes 3.200e-02 1.108e-01 0.289 0.7728
Med.Statin.LLDyes -4.882e-01 1.218e-01 -4.007 7.02e-05 ***
Med.all.antiplateletyes -9.108e-02 1.828e-01 -0.498 0.6185
GFR_MDRD -3.389e-03 2.866e-03 -1.182 0.2376
BMI -1.281e-02 1.385e-02 -0.925 0.3552
CAD_history -2.023e-01 1.175e-01 -1.721 0.0858 .
Stroke_history 2.240e-01 1.093e-01 2.050 0.0409 *
Peripheral.interv 2.654e-01 1.311e-01 2.024 0.0435 *
stenose50-70% 1.232e+00 8.614e-01 1.430 0.1533
stenose70-90% 1.238e+00 8.343e-01 1.484 0.1385
stenose90-99% 1.501e+00 8.343e-01 1.799 0.0726 .
stenose100% (Occlusion) 7.336e-01 1.021e+00 0.719 0.4726
stenose50-99% 1.069e+00 1.170e+00 0.914 0.3612
stenose70-99% -1.502e-01 1.437e+00 -0.105 0.9168
hsCRP_plasma 5.195e-05 6.976e-04 0.074 0.9407
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.165 on 533 degrees of freedom
Multiple R-squared: 0.09438, Adjusted R-squared: 0.0604
F-statistic: 2.777 on 20 and 533 DF, p-value: 6.359e-05
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_LN ' with ' VesselDensity_LN ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6R_pg_ug_2015_LN
Trait/outcome.............: VesselDensity_LN
Effect size...............: 0.057125
Standard error............: 0.056805
Odds ratio (effect size)..: 1.059
Lower 95% CI..............: 0.947
Upper 95% CI..............: 1.183
T-value...................: 1.005637
P-value...................: 0.3150463
R^2.......................: 0.094382
Adjusted r^2..............: 0.0604
Sample size of AE DB......: 2388
Sample size of model......: 554
Missing data %............: 76.80067
Analysis of MCP1_pg_ug_2015_LN.
- processing Macrophages_LN
Call:
lm(formula = currentDF[, PROTEIN] ~ Med.Statin.LLD + Stroke_history +
stenose, data = currentDF)
Coefficients:
(Intercept) Med.Statin.LLDyes Stroke_history stenose50-70% stenose70-90%
-1.8793 -0.2912 0.3131 1.1220 1.0316
stenose90-99% stenose100% (Occlusion) stenose50-99% stenose70-99%
0.7273 -0.4113 0.8206 1.5138
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose +
hsCRP_plasma, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-5.3039 -0.7758 0.0643 0.8035 3.3860
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.8812033 1.0731857 -0.821 0.4119
currentDF[, TRAIT] -0.0295689 0.0277731 -1.065 0.2875
Age -0.0042653 0.0066191 -0.644 0.5196
Gendermale 0.1462051 0.1139403 1.283 0.1999
Hypertension.compositeyes -0.1960709 0.1588148 -1.235 0.2175
DiabetesStatusDiabetes -0.1429423 0.1298394 -1.101 0.2714
SmokerCurrentyes -0.1277565 0.1141235 -1.119 0.2634
Med.Statin.LLDyes -0.2610838 0.1278712 -2.042 0.0416 *
Med.all.antiplateletyes -0.0781823 0.1808369 -0.432 0.6657
GFR_MDRD -0.0029277 0.0028995 -1.010 0.3130
BMI -0.0116576 0.0141153 -0.826 0.4092
CAD_history -0.1361524 0.1197991 -1.137 0.2562
Stroke_history 0.2928078 0.1137000 2.575 0.0103 *
Peripheral.interv 0.0717310 0.1334881 0.537 0.5912
stenose50-70% 1.0601418 0.7766663 1.365 0.1728
stenose70-90% 1.0090563 0.7470111 1.351 0.1773
stenose90-99% 0.7176677 0.7470206 0.961 0.3371
stenose100% (Occlusion) -0.5769898 0.9891836 -0.583 0.5599
stenose50-99% 0.9900753 1.1755751 0.842 0.4000
stenose70-99% 1.5784674 1.0497797 1.504 0.1332
hsCRP_plasma 0.0007688 0.0005068 1.517 0.1298
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.28 on 602 degrees of freedom
Multiple R-squared: 0.06338, Adjusted R-squared: 0.03226
F-statistic: 2.037 on 20 and 602 DF, p-value: 0.004992
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_LN ' with ' Macrophages_LN ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_LN
Trait/outcome.............: Macrophages_LN
Effect size...............: -0.029569
Standard error............: 0.027773
Odds ratio (effect size)..: 0.971
Lower 95% CI..............: 0.919
Upper 95% CI..............: 1.025
T-value...................: -1.064662
P-value...................: 0.2874558
R^2.......................: 0.063381
Adjusted r^2..............: 0.032264
Sample size of AE DB......: 2388
Sample size of model......: 623
Missing data %............: 73.91122
- processing SMC_LN
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + DiabetesStatus +
Med.Statin.LLD + Stroke_history + stenose, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] DiabetesStatusDiabetes Med.Statin.LLDyes Stroke_history
-1.88268 -0.09171 -0.18016 -0.29820 0.28716
stenose50-70% stenose70-90% stenose90-99% stenose100% (Occlusion) stenose50-99%
1.12462 1.07530 0.80391 -0.38417 0.90149
stenose70-99%
1.56396
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose +
hsCRP_plasma, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-5.1184 -0.7854 0.0493 0.8138 3.2620
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.6700995 1.0642423 -0.630 0.5292
currentDF[, TRAIT] -0.0872578 0.0368329 -2.369 0.0181 *
Age -0.0066517 0.0065169 -1.021 0.3078
Gendermale 0.0923885 0.1136494 0.813 0.4166
Hypertension.compositeyes -0.1893488 0.1561421 -1.213 0.2257
DiabetesStatusDiabetes -0.1700329 0.1298157 -1.310 0.1908
SmokerCurrentyes -0.1389901 0.1132600 -1.227 0.2202
Med.Statin.LLDyes -0.2803065 0.1261178 -2.223 0.0266 *
Med.all.antiplateletyes -0.1139180 0.1811412 -0.629 0.5297
GFR_MDRD -0.0024052 0.0029160 -0.825 0.4098
BMI -0.0130704 0.0138807 -0.942 0.3468
CAD_history -0.1011589 0.1195612 -0.846 0.3978
Stroke_history 0.2824235 0.1129619 2.500 0.0127 *
Peripheral.interv 0.0863623 0.1332206 0.648 0.5171
stenose50-70% 1.1040728 0.7757153 1.423 0.1552
stenose70-90% 1.0748336 0.7474338 1.438 0.1509
stenose90-99% 0.8133478 0.7479632 1.087 0.2773
stenose100% (Occlusion) -0.4990121 0.9892412 -0.504 0.6141
stenose50-99% 1.0758127 1.1750666 0.916 0.3603
stenose70-99% 1.6605722 1.0500528 1.581 0.1143
hsCRP_plasma 0.0005881 0.0004510 1.304 0.1927
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.28 on 609 degrees of freedom
Multiple R-squared: 0.06973, Adjusted R-squared: 0.03918
F-statistic: 2.282 on 20 and 609 DF, p-value: 0.001226
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_LN ' with ' SMC_LN ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_LN
Trait/outcome.............: SMC_LN
Effect size...............: -0.087258
Standard error............: 0.036833
Odds ratio (effect size)..: 0.916
Lower 95% CI..............: 0.853
Upper 95% CI..............: 0.985
T-value...................: -2.369014
P-value...................: 0.01814655
R^2.......................: 0.069727
Adjusted r^2..............: 0.039176
Sample size of AE DB......: 2388
Sample size of model......: 630
Missing data %............: 73.61809
- processing VesselDensity_LN
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Med.Statin.LLD +
Stroke_history, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Med.Statin.LLDyes Stroke_history
-0.7804 -0.1472 -0.2899 0.3511
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose +
hsCRP_plasma, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-5.1603 -0.7207 0.0469 0.8139 3.3145
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -3.642e-01 1.120e+00 -0.325 0.74524
currentDF[, TRAIT] -1.187e-01 6.272e-02 -1.893 0.05885 .
Age -6.080e-03 6.935e-03 -0.877 0.38104
Gendermale 1.729e-01 1.203e-01 1.437 0.15123
Hypertension.compositeyes -1.658e-01 1.688e-01 -0.982 0.32658
DiabetesStatusDiabetes -1.479e-01 1.399e-01 -1.057 0.29094
SmokerCurrentyes -1.308e-01 1.216e-01 -1.076 0.28225
Med.Statin.LLDyes -2.624e-01 1.350e-01 -1.943 0.05255 .
Med.all.antiplateletyes -6.918e-02 2.005e-01 -0.345 0.73022
GFR_MDRD -3.295e-03 3.167e-03 -1.040 0.29872
BMI -1.416e-02 1.475e-02 -0.960 0.33764
CAD_history -1.212e-01 1.289e-01 -0.940 0.34746
Stroke_history 3.304e-01 1.211e-01 2.728 0.00657 **
Peripheral.interv 2.054e-02 1.440e-01 0.143 0.88663
stenose50-70% 8.481e-01 8.003e-01 1.060 0.28977
stenose70-90% 8.732e-01 7.645e-01 1.142 0.25389
stenose90-99% 6.220e-01 7.639e-01 0.814 0.41586
stenose100% (Occlusion) -6.670e-01 1.010e+00 -0.660 0.50926
stenose50-99% 1.029e+00 1.198e+00 0.859 0.39075
stenose70-99% 1.565e+00 1.513e+00 1.034 0.30148
hsCRP_plasma -1.871e-05 7.805e-04 -0.024 0.98088
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.305 on 551 degrees of freedom
Multiple R-squared: 0.06408, Adjusted R-squared: 0.0301
F-statistic: 1.886 on 20 and 551 DF, p-value: 0.01145
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_LN ' with ' VesselDensity_LN ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_LN
Trait/outcome.............: VesselDensity_LN
Effect size...............: -0.118742
Standard error............: 0.06272
Odds ratio (effect size)..: 0.888
Lower 95% CI..............: 0.785
Upper 95% CI..............: 1.004
T-value...................: -1.893206
P-value...................: 0.05885423
R^2.......................: 0.064076
Adjusted r^2..............: 0.030104
Sample size of AE DB......: 2388
Sample size of model......: 572
Missing data %............: 76.0469 cat("Edit the column names...\n")Edit the column names...colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
"Beta", "s.e.m.",
"OR", "low95CI", "up95CI",
"T-value", "P-value", "r^2", "r^2_adj", "AE_N", "Model_N", "Perc_Miss")
cat("Correct the variable types...\n")Correct the variable types...GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)
# Save the data
cat("Writing results to Excel-file...\n")Writing results to Excel-file...### Univariate
library(openxlsx)
write.xlsx(GLM.results,
file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Con.Multi.Protein.PlaquePhenotypes.MODEL4.xlsx"),
row.names = FALSE, col.names = TRUE, sheetName = "Con.Multi.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")Removing intermediate files...rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)
Analysis of binary plaque traits as a function of serum/plaque IL6(R)/MCP1 levels.
GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN)) {
PROTEIN = TRAITS.PROTEIN[protein]
cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
for (trait in 1:length(TRAITS.BIN)) {
TRAIT = TRAITS.BIN[trait]
cat(paste0("\n- processing ",TRAIT,"\n\n"))
currentDF <- as.data.frame(AEDB.CEA %>%
dplyr::select(., PROTEIN, TRAIT, COVARIATES_M4) %>%
filter(complete.cases(.))) %>%
filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
# for debug
# print(DT::datatable(currentDF))
# print(nrow(currentDF))
# print(str(currentDF))
# print(class(currentDF[,TRAIT]))
### univariate
fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender +
Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose + hsCRP_plasma,
data = currentDF, family = binomial(link = "logit"))
model_step <- stepAIC(fit, direction = "both", trace = FALSE)
print(model_step)
print(summary(fit))
GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
GLM.results = rbind(GLM.results, GLM.results.TEMP)
}
}
Analysis of IL6_LN.
- processing CalcificationPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Hypertension.composite +
Med.all.antiplatelet + Peripheral.interv, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) Hypertension.compositeyes Med.all.antiplateletyes Peripheral.interv
-1.0880 0.7922 0.8796 -0.4787
Degrees of Freedom: 227 Total (i.e. Null); 224 Residual
Null Deviance: 311
Residual Deviance: 302 AIC: 310
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + hsCRP_plasma, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.9104 -1.1827 0.7429 1.0011 1.9740
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -1.467e+01 8.827e+02 -0.017 0.9867
currentDF[, PROTEIN] 8.888e-02 1.398e-01 0.636 0.5248
Age -6.796e-03 2.019e-02 -0.337 0.7365
Gendermale -1.165e-01 3.334e-01 -0.349 0.7268
Hypertension.compositeyes 1.044e+00 4.298e-01 2.429 0.0151 *
DiabetesStatusDiabetes -4.304e-01 4.153e-01 -1.036 0.3001
SmokerCurrentyes -2.732e-01 3.105e-01 -0.880 0.3789
Med.Statin.LLDyes -1.268e-01 3.186e-01 -0.398 0.6907
Med.all.antiplateletyes 1.237e+00 6.093e-01 2.031 0.0423 *
GFR_MDRD -8.006e-03 9.252e-03 -0.865 0.3869
BMI -3.583e-02 3.846e-02 -0.931 0.3516
CAD_history 4.087e-01 3.338e-01 1.224 0.2209
Stroke_history 3.007e-02 3.184e-01 0.094 0.9247
Peripheral.interv -4.737e-01 3.534e-01 -1.340 0.1801
stenose50-70% 1.621e+01 8.827e+02 0.018 0.9854
stenose70-90% 1.527e+01 8.827e+02 0.017 0.9862
stenose90-99% 1.459e+01 8.827e+02 0.017 0.9868
stenose100% (Occlusion) 1.619e+01 8.827e+02 0.018 0.9854
hsCRP_plasma 2.381e-04 1.477e-03 0.161 0.8719
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 310.99 on 227 degrees of freedom
Residual deviance: 289.33 on 209 degrees of freedom
AIC: 327.33
Number of Fisher Scoring iterations: 13
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_LN ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_LN
Trait/outcome.............: CalcificationPlaque
Effect size...............: 0.088879
Standard error............: 0.139763
Odds ratio (effect size)..: 1.093
Lower 95% CI..............: 0.831
Upper 95% CI..............: 1.437
Z-value...................: 0.635922
P-value...................: 0.5248275
Hosmer and Lemeshow r^2...: 0.069621
Cox and Snell r^2.........: 0.090591
Nagelkerke's pseudo r^2...: 0.121704
Sample size of AE DB......: 2388
Sample size of model......: 228
Missing data %............: 90.45226
- processing CollagenPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ 1, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept)
1.164
Degrees of Freedom: 226 Total (i.e. Null); 226 Residual
Null Deviance: 249.1
Residual Deviance: 249.1 AIC: 251.1
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + hsCRP_plasma, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.96913 0.00035 0.65960 0.76941 1.25599
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.807e+01 2.400e+03 0.008 0.9940
currentDF[, PROTEIN] -4.345e-02 1.592e-01 -0.273 0.7849
Age -3.032e-04 2.255e-02 -0.013 0.9893
Gendermale -1.847e-01 3.851e-01 -0.480 0.6314
Hypertension.compositeyes 1.506e-01 4.760e-01 0.316 0.7517
DiabetesStatusDiabetes 2.247e-01 4.826e-01 0.466 0.6414
SmokerCurrentyes 2.939e-01 3.591e-01 0.819 0.4130
Med.Statin.LLDyes -2.610e-01 3.679e-01 -0.710 0.4780
Med.all.antiplateletyes 1.054e+00 5.928e-01 1.778 0.0755 .
GFR_MDRD -7.672e-03 1.060e-02 -0.724 0.4693
BMI -5.653e-02 4.244e-02 -1.332 0.1829
CAD_history 2.094e-01 3.775e-01 0.555 0.5792
Stroke_history 3.602e-01 3.784e-01 0.952 0.3411
Peripheral.interv -2.213e-01 3.930e-01 -0.563 0.5733
stenose50-70% -3.942e-01 2.671e+03 0.000 0.9999
stenose70-90% -1.559e+01 2.400e+03 -0.006 0.9948
stenose90-99% -1.589e+01 2.400e+03 -0.007 0.9947
stenose100% (Occlusion) 2.999e-01 2.762e+03 0.000 0.9999
hsCRP_plasma -3.611e-05 1.754e-03 -0.021 0.9836
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 249.08 on 226 degrees of freedom
Residual deviance: 236.35 on 208 degrees of freedom
AIC: 274.35
Number of Fisher Scoring iterations: 15
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_LN ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_LN
Trait/outcome.............: CollagenPlaque
Effect size...............: -0.043447
Standard error............: 0.159165
Odds ratio (effect size)..: 0.957
Lower 95% CI..............: 0.701
Upper 95% CI..............: 1.308
Z-value...................: -0.272968
P-value...................: 0.7848776
Hosmer and Lemeshow r^2...: 0.051106
Cox and Snell r^2.........: 0.054533
Nagelkerke's pseudo r^2...: 0.081855
Sample size of AE DB......: 2388
Sample size of model......: 227
Missing data %............: 90.49414
- processing Fat10Perc
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Gender + Hypertension.composite,
family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) Gendermale Hypertension.compositeyes
0.0896 0.5880 1.1666
Degrees of Freedom: 227 Total (i.e. Null); 225 Residual
Null Deviance: 223.7
Residual Deviance: 213.1 AIC: 219.1
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + hsCRP_plasma, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.3036 0.3624 0.5214 0.6599 1.2796
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.430e+01 2.400e+03 0.006 0.99525
currentDF[, PROTEIN] 2.696e-02 1.700e-01 0.159 0.87400
Age -4.561e-04 2.558e-02 -0.018 0.98577
Gendermale 6.874e-01 4.010e-01 1.714 0.08646 .
Hypertension.compositeyes 1.198e+00 4.578e-01 2.617 0.00886 **
DiabetesStatusDiabetes -4.552e-01 5.144e-01 -0.885 0.37630
SmokerCurrentyes 4.572e-01 4.162e-01 1.098 0.27204
Med.Statin.LLDyes 1.214e-01 4.076e-01 0.298 0.76579
Med.all.antiplateletyes 3.352e-01 7.061e-01 0.475 0.63496
GFR_MDRD -1.454e-02 1.220e-02 -1.191 0.23351
BMI 5.048e-02 5.070e-02 0.996 0.31943
CAD_history -4.837e-02 4.248e-01 -0.114 0.90935
Stroke_history -2.502e-01 4.069e-01 -0.615 0.53863
Peripheral.interv -4.368e-01 4.284e-01 -1.019 0.30798
stenose50-70% -1.654e+01 2.400e+03 -0.007 0.99450
stenose70-90% -1.461e+01 2.400e+03 -0.006 0.99514
stenose90-99% -1.511e+01 2.400e+03 -0.006 0.99498
stenose100% (Occlusion) 1.041e+00 2.706e+03 0.000 0.99969
hsCRP_plasma -7.061e-04 1.826e-03 -0.387 0.69894
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 223.68 on 227 degrees of freedom
Residual deviance: 201.20 on 209 degrees of freedom
AIC: 239.2
Number of Fisher Scoring iterations: 15
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_LN ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_LN
Trait/outcome.............: Fat10Perc
Effect size...............: 0.026956
Standard error............: 0.169988
Odds ratio (effect size)..: 1.027
Lower 95% CI..............: 0.736
Upper 95% CI..............: 1.434
Z-value...................: 0.158577
P-value...................: 0.8740024
Hosmer and Lemeshow r^2...: 0.100502
Cox and Snell r^2.........: 0.093892
Nagelkerke's pseudo r^2...: 0.150208
Sample size of AE DB......: 2388
Sample size of model......: 228
Missing data %............: 90.45226
- processing IPH
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Gender, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) Gendermale
0.6466 0.7704
Degrees of Freedom: 227 Total (i.e. Null); 226 Residual
Null Deviance: 249.6
Residual Deviance: 244.3 AIC: 248.3
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + hsCRP_plasma, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.1948 0.2843 0.6345 0.7479 1.1973
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 12.719295 882.748263 0.014 0.9885
currentDF[, PROTEIN] -0.158259 0.154518 -1.024 0.3057
Age 0.021323 0.022751 0.937 0.3487
Gendermale 0.787010 0.365125 2.155 0.0311 *
Hypertension.compositeyes 0.556927 0.446202 1.248 0.2120
DiabetesStatusDiabetes -0.311891 0.462582 -0.674 0.5002
SmokerCurrentyes 0.552119 0.367039 1.504 0.1325
Med.Statin.LLDyes 0.032643 0.366678 0.089 0.9291
Med.all.antiplateletyes -0.322585 0.731270 -0.441 0.6591
GFR_MDRD 0.002551 0.010865 0.235 0.8143
BMI 0.010367 0.042470 0.244 0.8072
CAD_history 0.002075 0.380029 0.005 0.9956
Stroke_history -0.168600 0.368940 -0.457 0.6477
Peripheral.interv -0.094236 0.403477 -0.234 0.8153
stenose50-70% -13.221860 882.744597 -0.015 0.9880
stenose70-90% -13.718958 882.743674 -0.016 0.9876
stenose90-99% -13.629294 882.743648 -0.015 0.9877
stenose100% (Occlusion) -14.220158 882.744840 -0.016 0.9871
hsCRP_plasma 0.005093 0.005303 0.960 0.3369
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 249.62 on 227 degrees of freedom
Residual deviance: 235.94 on 209 degrees of freedom
AIC: 273.94
Number of Fisher Scoring iterations: 13
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_LN ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_LN
Trait/outcome.............: IPH
Effect size...............: -0.158259
Standard error............: 0.154518
Odds ratio (effect size)..: 0.854
Lower 95% CI..............: 0.631
Upper 95% CI..............: 1.156
Z-value...................: -1.024214
P-value...................: 0.3057341
Hosmer and Lemeshow r^2...: 0.054812
Cox and Snell r^2.........: 0.058244
Nagelkerke's pseudo r^2...: 0.087533
Sample size of AE DB......: 2388
Sample size of model......: 228
Missing data %............: 90.45226
Analysis of MCP1_LN.
- processing CalcificationPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Hypertension.composite +
DiabetesStatus + Peripheral.interv, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) Hypertension.compositeyes DiabetesStatusDiabetes Peripheral.interv
0.09271 0.58088 -0.65466 -0.62140
Degrees of Freedom: 278 Total (i.e. Null); 275 Residual
Null Deviance: 378.8
Residual Deviance: 368.2 AIC: 376.2
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + hsCRP_plasma, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.8758 -1.2049 0.7323 0.9929 1.9463
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -1.470e+01 8.827e+02 -0.017 0.9867
currentDF[, PROTEIN] -2.190e-01 1.532e-01 -1.430 0.1528
Age 1.112e-02 1.734e-02 0.641 0.5214
Gendermale 1.861e-01 2.961e-01 0.628 0.5298
Hypertension.compositeyes 5.904e-01 3.662e-01 1.612 0.1069
DiabetesStatusDiabetes -8.080e-01 3.581e-01 -2.257 0.0240 *
SmokerCurrentyes 1.574e-01 2.809e-01 0.560 0.5752
Med.Statin.LLDyes -1.235e-01 2.946e-01 -0.419 0.6752
Med.all.antiplateletyes 9.068e-01 5.242e-01 1.730 0.0836 .
GFR_MDRD -2.241e-03 7.880e-03 -0.284 0.7761
BMI 6.325e-03 3.309e-02 0.191 0.8484
CAD_history 1.825e-01 2.990e-01 0.610 0.5417
Stroke_history -4.221e-01 2.847e-01 -1.483 0.1382
Peripheral.interv -6.863e-01 3.240e-01 -2.118 0.0342 *
stenose50-70% 1.472e+01 8.827e+02 0.017 0.9867
stenose70-90% 1.465e+01 8.827e+02 0.017 0.9868
stenose90-99% 1.398e+01 8.827e+02 0.016 0.9874
stenose100% (Occlusion) 1.509e+01 8.827e+02 0.017 0.9864
hsCRP_plasma 9.254e-05 1.564e-03 0.059 0.9528
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 378.82 on 278 degrees of freedom
Residual deviance: 354.39 on 260 degrees of freedom
AIC: 392.39
Number of Fisher Scoring iterations: 13
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_LN ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_LN
Trait/outcome.............: CalcificationPlaque
Effect size...............: -0.218974
Standard error............: 0.153168
Odds ratio (effect size)..: 0.803
Lower 95% CI..............: 0.595
Upper 95% CI..............: 1.085
Z-value...................: -1.429634
P-value...................: 0.1528221
Hosmer and Lemeshow r^2...: 0.064487
Cox and Snell r^2.........: 0.083836
Nagelkerke's pseudo r^2...: 0.112869
Sample size of AE DB......: 2388
Sample size of model......: 279
Missing data %............: 88.31658
- processing CollagenPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN],
family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN]
4.1409 -0.5963
Degrees of Freedom: 277 Total (i.e. Null); 276 Residual
Null Deviance: 292.6
Residual Deviance: 280.9 AIC: 284.9
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + hsCRP_plasma, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.40189 0.04367 0.59854 0.75261 1.27383
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 2.019e+01 2.400e+03 0.008 0.99329
currentDF[, PROTEIN] -6.303e-01 2.010e-01 -3.136 0.00171 **
Age -2.621e-03 2.060e-02 -0.127 0.89876
Gendermale -4.887e-02 3.644e-01 -0.134 0.89331
Hypertension.compositeyes 2.694e-02 4.346e-01 0.062 0.95057
DiabetesStatusDiabetes 1.557e-01 4.407e-01 0.353 0.72396
SmokerCurrentyes 3.493e-01 3.437e-01 1.016 0.30954
Med.Statin.LLDyes -8.183e-03 3.463e-01 -0.024 0.98115
Med.all.antiplateletyes 7.947e-01 5.644e-01 1.408 0.15911
GFR_MDRD -5.321e-03 9.615e-03 -0.553 0.57996
BMI -1.097e-02 4.108e-02 -0.267 0.78947
CAD_history 1.156e-01 3.530e-01 0.327 0.74333
Stroke_history 3.336e-01 3.580e-01 0.932 0.35152
Peripheral.interv -4.883e-01 3.716e-01 -1.314 0.18884
stenose50-70% -6.988e-01 2.624e+03 0.000 0.99979
stenose70-90% -1.575e+01 2.400e+03 -0.007 0.99476
stenose90-99% -1.611e+01 2.400e+03 -0.007 0.99464
stenose100% (Occlusion) -7.586e-01 2.699e+03 0.000 0.99978
hsCRP_plasma -7.904e-05 1.698e-03 -0.047 0.96288
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 292.56 on 277 degrees of freedom
Residual deviance: 269.41 on 259 degrees of freedom
AIC: 307.41
Number of Fisher Scoring iterations: 15
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_LN ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_LN
Trait/outcome.............: CollagenPlaque
Effect size...............: -0.630266
Standard error............: 0.200995
Odds ratio (effect size)..: 0.532
Lower 95% CI..............: 0.359
Upper 95% CI..............: 0.79
Z-value...................: -3.135732
P-value...................: 0.001714256
Hosmer and Lemeshow r^2...: 0.079123
Cox and Snell r^2.........: 0.079893
Nagelkerke's pseudo r^2...: 0.122745
Sample size of AE DB......: 2388
Sample size of model......: 278
Missing data %............: 88.35846
- processing Fat10Perc
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Gender + Hypertension.composite, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Gendermale Hypertension.compositeyes
-2.9243 0.6453 0.6773 1.0243
Degrees of Freedom: 278 Total (i.e. Null); 275 Residual
Null Deviance: 290.5
Residual Deviance: 264.7 AIC: 272.7
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + hsCRP_plasma, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.3824 0.2949 0.4989 0.6958 1.6759
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 7.540e+00 8.827e+02 0.009 0.993185
currentDF[, PROTEIN] 7.111e-01 1.940e-01 3.666 0.000246 ***
Age 3.819e-03 2.146e-02 0.178 0.858758
Gendermale 7.301e-01 3.458e-01 2.111 0.034740 *
Hypertension.compositeyes 9.482e-01 4.234e-01 2.240 0.025122 *
DiabetesStatusDiabetes -2.394e-01 4.376e-01 -0.547 0.584309
SmokerCurrentyes 4.139e-01 3.533e-01 1.172 0.241352
Med.Statin.LLDyes 1.285e-02 3.729e-01 0.034 0.972520
Med.all.antiplateletyes 5.448e-01 5.944e-01 0.917 0.359365
GFR_MDRD -7.860e-03 1.005e-02 -0.782 0.434221
BMI 3.652e-02 4.134e-02 0.883 0.377006
CAD_history -1.925e-02 3.799e-01 -0.051 0.959579
Stroke_history 1.594e-01 3.668e-01 0.434 0.663940
Peripheral.interv 1.323e-02 4.048e-01 0.033 0.973932
stenose50-70% -1.418e+01 8.827e+02 -0.016 0.987184
stenose70-90% -1.167e+01 8.827e+02 -0.013 0.989454
stenose90-99% -1.218e+01 8.827e+02 -0.014 0.988990
stenose100% (Occlusion) -1.160e+01 8.827e+02 -0.013 0.989516
hsCRP_plasma -3.983e-04 1.983e-03 -0.201 0.840842
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 290.48 on 278 degrees of freedom
Residual deviance: 252.63 on 260 degrees of freedom
AIC: 290.63
Number of Fisher Scoring iterations: 13
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_LN ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_LN
Trait/outcome.............: Fat10Perc
Effect size...............: 0.711076
Standard error............: 0.193961
Odds ratio (effect size)..: 2.036
Lower 95% CI..............: 1.392
Upper 95% CI..............: 2.978
Z-value...................: 3.666076
P-value...................: 0.0002463009
Hosmer and Lemeshow r^2...: 0.13032
Cox and Snell r^2.........: 0.12688
Nagelkerke's pseudo r^2...: 0.19612
Sample size of AE DB......: 2388
Sample size of model......: 279
Missing data %............: 88.31658
- processing IPH
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Gender, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) Gendermale
0.5645 0.8471
Degrees of Freedom: 278 Total (i.e. Null); 277 Residual
Null Deviance: 309.9
Residual Deviance: 301.7 AIC: 305.7
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + hsCRP_plasma, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.2386 0.2287 0.6171 0.7649 1.4284
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 13.273572 882.747406 0.015 0.98800
currentDF[, PROTEIN] -0.217854 0.178046 -1.224 0.22111
Age 0.008607 0.019930 0.432 0.66585
Gendermale 1.005564 0.323913 3.104 0.00191 **
Hypertension.compositeyes 0.085207 0.409705 0.208 0.83525
DiabetesStatusDiabetes -0.362545 0.386058 -0.939 0.34768
SmokerCurrentyes 0.365995 0.325207 1.125 0.26041
Med.Statin.LLDyes -0.383866 0.348667 -1.101 0.27092
Med.all.antiplateletyes 0.594389 0.542350 1.096 0.27310
GFR_MDRD -0.001712 0.009224 -0.186 0.85273
BMI 0.015316 0.036457 0.420 0.67440
CAD_history 0.166401 0.346892 0.480 0.63145
Stroke_history -0.020672 0.331278 -0.062 0.95024
Peripheral.interv 0.215540 0.383531 0.562 0.57412
stenose50-70% -12.581005 882.744345 -0.014 0.98863
stenose70-90% -13.252706 882.743558 -0.015 0.98802
stenose90-99% -13.155460 882.743548 -0.015 0.98811
stenose100% (Occlusion) -13.618488 882.744627 -0.015 0.98769
hsCRP_plasma 0.003153 0.004361 0.723 0.46965
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 309.88 on 278 degrees of freedom
Residual deviance: 291.99 on 260 degrees of freedom
AIC: 329.99
Number of Fisher Scoring iterations: 13
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_LN ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_LN
Trait/outcome.............: IPH
Effect size...............: -0.217854
Standard error............: 0.178046
Odds ratio (effect size)..: 0.804
Lower 95% CI..............: 0.567
Upper 95% CI..............: 1.14
Z-value...................: -1.223586
P-value...................: 0.2211086
Hosmer and Lemeshow r^2...: 0.05772
Cox and Snell r^2.........: 0.062097
Nagelkerke's pseudo r^2...: 0.09259
Sample size of AE DB......: 2388
Sample size of model......: 279
Missing data %............: 88.31658
Analysis of IL6_pg_ug_2015_LN.
- processing CalcificationPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + SmokerCurrent +
CAD_history + Peripheral.interv + stenose, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) Age SmokerCurrentyes CAD_history Peripheral.interv
-2.03767 0.01906 0.31759 0.28534 -0.39470
stenose50-70% stenose70-90% stenose90-99% stenose100% (Occlusion) stenose70-99%
-0.26332 0.44676 0.69678 1.70987 -14.15643
Degrees of Freedom: 613 Total (i.e. Null); 604 Residual
Null Deviance: 849.9
Residual Deviance: 824.6 AIC: 844.6
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + hsCRP_plasma, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.6386 -1.1238 -0.7819 1.1603 1.7127
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -3.173e+00 1.756e+00 -1.807 0.0708 .
currentDF[, PROTEIN] -1.540e-02 5.886e-02 -0.262 0.7936
Age 2.147e-02 1.055e-02 2.034 0.0420 *
Gendermale 3.077e-02 1.826e-01 0.169 0.8662
Hypertension.compositeyes 2.945e-01 2.552e-01 1.154 0.2485
DiabetesStatusDiabetes -2.425e-02 2.117e-01 -0.115 0.9088
SmokerCurrentyes 3.489e-01 1.844e-01 1.892 0.0585 .
Med.Statin.LLDyes -2.639e-01 2.026e-01 -1.302 0.1928
Med.all.antiplateletyes 6.589e-02 2.938e-01 0.224 0.8226
GFR_MDRD 4.745e-03 4.713e-03 1.007 0.3140
BMI 1.438e-02 2.247e-02 0.640 0.5220
CAD_history 2.970e-01 1.954e-01 1.520 0.1286
Stroke_history 5.443e-03 1.840e-01 0.030 0.9764
Peripheral.interv -3.830e-01 2.158e-01 -1.775 0.0759 .
stenose50-70% -1.919e-01 1.300e+00 -0.148 0.8826
stenose70-90% 5.145e-01 1.251e+00 0.411 0.6808
stenose90-99% 7.534e-01 1.251e+00 0.602 0.5469
stenose100% (Occlusion) 1.846e+00 1.719e+00 1.074 0.2827
stenose70-99% -1.407e+01 4.293e+02 -0.033 0.9739
hsCRP_plasma -3.988e-04 7.677e-04 -0.519 0.6034
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 849.91 on 613 degrees of freedom
Residual deviance: 819.86 on 594 degrees of freedom
AIC: 859.86
Number of Fisher Scoring iterations: 13
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_LN ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_pg_ug_2015_LN
Trait/outcome.............: CalcificationPlaque
Effect size...............: -0.015399
Standard error............: 0.05886
Odds ratio (effect size)..: 0.985
Lower 95% CI..............: 0.877
Upper 95% CI..............: 1.105
Z-value...................: -0.261629
P-value...................: 0.7936074
Hosmer and Lemeshow r^2...: 0.035358
Cox and Snell r^2.........: 0.047765
Nagelkerke's pseudo r^2...: 0.063731
Sample size of AE DB......: 2388
Sample size of model......: 614
Missing data %............: 74.28811
- processing CollagenPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
SmokerCurrent + CAD_history, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] SmokerCurrentyes CAD_history
0.2877 -0.2105 0.4717 0.5078
Degrees of Freedom: 616 Total (i.e. Null); 613 Residual
Null Deviance: 658.4
Residual Deviance: 639.6 AIC: 647.6
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + hsCRP_plasma, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.3103 0.3936 0.6280 0.7490 1.1420
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.236e+01 8.343e+02 0.015 0.9882
currentDF[, PROTEIN] -2.279e-01 7.376e-02 -3.090 0.0020 **
Age 6.682e-03 1.232e-02 0.543 0.5874
Gendermale 7.361e-02 2.168e-01 0.340 0.7342
Hypertension.compositeyes 1.575e-01 2.883e-01 0.546 0.5849
DiabetesStatusDiabetes 2.084e-01 2.609e-01 0.799 0.4244
SmokerCurrentyes 5.500e-01 2.293e-01 2.399 0.0165 *
Med.Statin.LLDyes -1.219e-01 2.420e-01 -0.504 0.6145
Med.all.antiplateletyes 5.306e-01 3.297e-01 1.609 0.1075
GFR_MDRD 2.483e-03 5.598e-03 0.444 0.6574
BMI 2.501e-02 2.806e-02 0.891 0.3727
CAD_history 5.373e-01 2.479e-01 2.167 0.0302 *
Stroke_history 3.066e-01 2.238e-01 1.370 0.1707
Peripheral.interv 1.005e-01 2.631e-01 0.382 0.7024
stenose50-70% -1.414e+01 8.343e+02 -0.017 0.9865
stenose70-90% -1.420e+01 8.343e+02 -0.017 0.9864
stenose90-99% -1.414e+01 8.343e+02 -0.017 0.9865
stenose100% (Occlusion) 7.104e-01 1.070e+03 0.001 0.9995
stenose70-99% -1.442e+01 8.343e+02 -0.017 0.9862
hsCRP_plasma -7.973e-04 7.313e-04 -1.090 0.2756
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 658.36 on 616 degrees of freedom
Residual deviance: 627.52 on 597 degrees of freedom
AIC: 667.52
Number of Fisher Scoring iterations: 14
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_LN ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_pg_ug_2015_LN
Trait/outcome.............: CollagenPlaque
Effect size...............: -0.227904
Standard error............: 0.073764
Odds ratio (effect size)..: 0.796
Lower 95% CI..............: 0.689
Upper 95% CI..............: 0.92
Z-value...................: -3.089637
P-value...................: 0.00200401
Hosmer and Lemeshow r^2...: 0.046845
Cox and Snell r^2.........: 0.048756
Nagelkerke's pseudo r^2...: 0.074327
Sample size of AE DB......: 2388
Sample size of model......: 617
Missing data %............: 74.16248
- processing Fat10Perc
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Gender + Peripheral.interv, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Gendermale Peripheral.interv
1.8788 0.4179 0.8153 -0.4469
Degrees of Freedom: 616 Total (i.e. Null); 613 Residual
Null Deviance: 743.2
Residual Deviance: 680.3 AIC: 688.3
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + hsCRP_plasma, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.2635 -1.0430 0.6076 0.8200 2.0560
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 14.839255 506.969431 0.029 0.9766
currentDF[, PROTEIN] 0.414706 0.071877 5.770 7.94e-09 ***
Age 0.006483 0.011992 0.541 0.5888
Gendermale 0.844744 0.201005 4.203 2.64e-05 ***
Hypertension.compositeyes 0.124301 0.286219 0.434 0.6641
DiabetesStatusDiabetes -0.036004 0.241122 -0.149 0.8813
SmokerCurrentyes -0.010489 0.210837 -0.050 0.9603
Med.Statin.LLDyes -0.055825 0.235469 -0.237 0.8126
Med.all.antiplateletyes 0.006388 0.330784 0.019 0.9846
GFR_MDRD -0.001176 0.005412 -0.217 0.8280
BMI 0.008795 0.024724 0.356 0.7220
CAD_history -0.052522 0.222360 -0.236 0.8133
Stroke_history 0.225596 0.216332 1.043 0.2970
Peripheral.interv -0.462486 0.233665 -1.979 0.0478 *
stenose50-70% -14.082511 506.967579 -0.028 0.9778
stenose70-90% -13.716572 506.967461 -0.027 0.9784
stenose90-99% -13.606310 506.967460 -0.027 0.9786
stenose100% (Occlusion) -15.178060 506.968637 -0.030 0.9761
stenose70-99% -14.767795 506.968856 -0.029 0.9768
hsCRP_plasma 0.000822 0.001299 0.633 0.5268
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 743.18 on 616 degrees of freedom
Residual deviance: 671.07 on 597 degrees of freedom
AIC: 711.07
Number of Fisher Scoring iterations: 13
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_LN ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_pg_ug_2015_LN
Trait/outcome.............: Fat10Perc
Effect size...............: 0.414706
Standard error............: 0.071877
Odds ratio (effect size)..: 1.514
Lower 95% CI..............: 1.315
Upper 95% CI..............: 1.743
Z-value...................: 5.769662
P-value...................: 7.943079e-09
Hosmer and Lemeshow r^2...: 0.097029
Cox and Snell r^2.........: 0.110301
Nagelkerke's pseudo r^2...: 0.157536
Sample size of AE DB......: 2388
Sample size of model......: 617
Missing data %............: 74.16248
- processing IPH
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Gender + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) Gendermale SmokerCurrentyes Med.Statin.LLDyes Med.all.antiplateletyes
-0.2863 0.6645 0.4139 -0.3598 0.4213
Degrees of Freedom: 615 Total (i.e. Null); 611 Residual
Null Deviance: 828.8
Residual Deviance: 806.9 AIC: 816.9
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + hsCRP_plasma, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.0797 -1.2434 0.8004 0.9968 1.5487
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.6679016 1.7675384 0.378 0.705526
currentDF[, PROTEIN] 0.0853773 0.0607429 1.406 0.159857
Age -0.0007668 0.0107421 -0.071 0.943095
Gendermale 0.7046627 0.1860325 3.788 0.000152 ***
Hypertension.compositeyes -0.3427477 0.2654362 -1.291 0.196613
DiabetesStatusDiabetes -0.1475819 0.2139819 -0.690 0.490387
SmokerCurrentyes 0.3944830 0.1922973 2.051 0.040226 *
Med.Statin.LLDyes -0.3594273 0.2126356 -1.690 0.090962 .
Med.all.antiplateletyes 0.3905767 0.2954866 1.322 0.186232
GFR_MDRD -0.0050159 0.0048312 -1.038 0.299158
BMI 0.0091376 0.0228338 0.400 0.689024
CAD_history 0.2170375 0.2011279 1.079 0.280542
Stroke_history 0.2148625 0.1908222 1.126 0.260173
Peripheral.interv 0.0992787 0.2211831 0.449 0.653538
stenose50-70% -0.7347676 1.2830633 -0.573 0.566870
stenose70-90% -0.4118810 1.2418707 -0.332 0.740145
stenose90-99% -0.1347980 1.2418505 -0.109 0.913563
stenose100% (Occlusion) -0.9043600 1.6034061 -0.564 0.572738
stenose70-99% 0.3900736 1.7017441 0.229 0.818698
hsCRP_plasma -0.0006036 0.0007987 -0.756 0.449817
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 828.82 on 615 degrees of freedom
Residual deviance: 794.18 on 596 degrees of freedom
AIC: 834.18
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_LN ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_pg_ug_2015_LN
Trait/outcome.............: IPH
Effect size...............: 0.085377
Standard error............: 0.060743
Odds ratio (effect size)..: 1.089
Lower 95% CI..............: 0.967
Upper 95% CI..............: 1.227
Z-value...................: 1.405553
P-value...................: 0.1598569
Hosmer and Lemeshow r^2...: 0.041805
Cox and Snell r^2.........: 0.054696
Nagelkerke's pseudo r^2...: 0.073954
Sample size of AE DB......: 2388
Sample size of model......: 616
Missing data %............: 74.20436
Analysis of IL6R_pg_ug_2015_LN.
- processing CalcificationPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + SmokerCurrent +
CAD_history + Peripheral.interv + stenose, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) Age SmokerCurrentyes CAD_history Peripheral.interv
-1.39761 0.01805 0.26766 0.28298 -0.44359
stenose50-70% stenose70-90% stenose90-99% stenose100% (Occlusion) stenose50-99%
-0.76341 -0.08234 0.14517 1.15737 -15.67282
stenose70-99%
-15.68045
Degrees of Freedom: 618 Total (i.e. Null); 608 Residual
Null Deviance: 856.9
Residual Deviance: 830.8 AIC: 852.8
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + hsCRP_plasma, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.5341 -1.1333 -0.7798 1.1685 1.7335
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -2.304e+00 1.868e+00 -1.233 0.2174
currentDF[, PROTEIN] -2.550e-02 7.191e-02 -0.355 0.7229
Age 2.039e-02 1.051e-02 1.939 0.0525 .
Gendermale -8.156e-03 1.812e-01 -0.045 0.9641
Hypertension.compositeyes 2.760e-01 2.520e-01 1.095 0.2734
DiabetesStatusDiabetes -1.014e-01 2.113e-01 -0.480 0.6314
SmokerCurrentyes 2.921e-01 1.829e-01 1.597 0.1103
Med.Statin.LLDyes -1.654e-01 2.047e-01 -0.808 0.4190
Med.all.antiplateletyes 1.379e-01 2.936e-01 0.470 0.6386
GFR_MDRD 4.026e-03 4.683e-03 0.860 0.3899
BMI 3.436e-03 2.296e-02 0.150 0.8810
CAD_history 2.917e-01 1.947e-01 1.498 0.1340
Stroke_history -1.209e-02 1.823e-01 -0.066 0.9471
Peripheral.interv -4.342e-01 2.163e-01 -2.007 0.0447 *
stenose50-70% -6.652e-01 1.476e+00 -0.451 0.6523
stenose70-90% 2.555e-02 1.434e+00 0.018 0.9858
stenose90-99% 2.454e-01 1.436e+00 0.171 0.8643
stenose100% (Occlusion) 1.308e+00 1.854e+00 0.706 0.4803
stenose50-99% -1.566e+01 1.029e+03 -0.015 0.9879
stenose70-99% -1.564e+01 7.120e+02 -0.022 0.9825
hsCRP_plasma -4.107e-04 7.569e-04 -0.543 0.5874
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 856.94 on 618 degrees of freedom
Residual deviance: 827.41 on 598 degrees of freedom
AIC: 869.41
Number of Fisher Scoring iterations: 14
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_LN ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6R_pg_ug_2015_LN
Trait/outcome.............: CalcificationPlaque
Effect size...............: -0.025502
Standard error............: 0.071908
Odds ratio (effect size)..: 0.975
Lower 95% CI..............: 0.847
Upper 95% CI..............: 1.122
Z-value...................: -0.354646
P-value...................: 0.7228549
Hosmer and Lemeshow r^2...: 0.034457
Cox and Snell r^2.........: 0.046582
Nagelkerke's pseudo r^2...: 0.062149
Sample size of AE DB......: 2388
Sample size of model......: 619
Missing data %............: 74.07873
- processing CollagenPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ SmokerCurrent +
Med.all.antiplatelet + CAD_history, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) SmokerCurrentyes Med.all.antiplateletyes CAD_history
0.4945 0.4512 0.4820 0.5581
Degrees of Freedom: 621 Total (i.e. Null); 618 Residual
Null Deviance: 665.8
Residual Deviance: 653.9 AIC: 661.9
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + hsCRP_plasma, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.2601 0.4421 0.6377 0.7667 1.1349
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.310e+01 1.011e+03 0.013 0.9897
currentDF[, PROTEIN] 2.674e-03 8.620e-02 0.031 0.9753
Age 9.604e-03 1.224e-02 0.784 0.4328
Gendermale 1.461e-01 2.117e-01 0.690 0.4902
Hypertension.compositeyes 1.037e-01 2.844e-01 0.365 0.7153
DiabetesStatusDiabetes 2.294e-01 2.589e-01 0.886 0.3755
SmokerCurrentyes 5.379e-01 2.259e-01 2.381 0.0173 *
Med.Statin.LLDyes -2.637e-02 2.395e-01 -0.110 0.9123
Med.all.antiplateletyes 5.600e-01 3.220e-01 1.739 0.0820 .
GFR_MDRD 1.793e-03 5.543e-03 0.323 0.7464
BMI 2.298e-02 2.773e-02 0.829 0.4072
CAD_history 5.430e-01 2.441e-01 2.224 0.0261 *
Stroke_history 1.959e-01 2.191e-01 0.894 0.3713
Peripheral.interv 1.392e-01 2.614e-01 0.532 0.5944
stenose50-70% -1.431e+01 1.011e+03 -0.014 0.9887
stenose70-90% -1.446e+01 1.011e+03 -0.014 0.9886
stenose90-99% -1.431e+01 1.011e+03 -0.014 0.9887
stenose100% (Occlusion) 3.082e-01 1.229e+03 0.000 0.9998
stenose50-99% -3.990e-01 1.442e+03 0.000 0.9998
stenose70-99% -1.468e+01 1.011e+03 -0.015 0.9884
hsCRP_plasma -8.408e-04 7.371e-04 -1.141 0.2540
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 665.84 on 621 degrees of freedom
Residual deviance: 643.59 on 601 degrees of freedom
AIC: 685.59
Number of Fisher Scoring iterations: 14
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_LN ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6R_pg_ug_2015_LN
Trait/outcome.............: CollagenPlaque
Effect size...............: 0.002674
Standard error............: 0.086197
Odds ratio (effect size)..: 1.003
Lower 95% CI..............: 0.847
Upper 95% CI..............: 1.187
Z-value...................: 0.031024
P-value...................: 0.9752504
Hosmer and Lemeshow r^2...: 0.033428
Cox and Snell r^2.........: 0.035151
Nagelkerke's pseudo r^2...: 0.05349
Sample size of AE DB......: 2388
Sample size of model......: 622
Missing data %............: 73.9531
- processing Fat10Perc
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Gender + Stroke_history + Peripheral.interv, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Gendermale Stroke_history Peripheral.interv
0.7423 0.2002 0.8589 0.3515 -0.4777
Degrees of Freedom: 621 Total (i.e. Null); 617 Residual
Null Deviance: 750.2
Residual Deviance: 714.1 AIC: 724.1
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + hsCRP_plasma, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.0759 -1.1783 0.6695 0.8326 1.7892
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.378e+01 6.241e+02 0.022 0.9824
currentDF[, PROTEIN] 1.982e-01 7.742e-02 2.561 0.0105 *
Age 9.299e-03 1.162e-02 0.800 0.4237
Gendermale 8.983e-01 1.954e-01 4.597 4.28e-06 ***
Hypertension.compositeyes 1.373e-01 2.760e-01 0.498 0.6188
DiabetesStatusDiabetes -6.609e-03 2.343e-01 -0.028 0.9775
SmokerCurrentyes 6.358e-02 2.053e-01 0.310 0.7568
Med.Statin.LLDyes 4.918e-02 2.321e-01 0.212 0.8322
Med.all.antiplateletyes 5.701e-02 3.244e-01 0.176 0.8605
GFR_MDRD -2.183e-04 5.287e-03 -0.041 0.9671
BMI -3.735e-03 2.513e-02 -0.149 0.8818
CAD_history -1.611e-01 2.163e-01 -0.745 0.4563
Stroke_history 3.315e-01 2.104e-01 1.575 0.1152
Peripheral.interv -4.879e-01 2.286e-01 -2.135 0.0328 *
stenose50-70% -1.408e+01 6.241e+02 -0.023 0.9820
stenose70-90% -1.372e+01 6.241e+02 -0.022 0.9825
stenose90-99% -1.375e+01 6.241e+02 -0.022 0.9824
stenose100% (Occlusion) -1.502e+01 6.241e+02 -0.024 0.9808
stenose50-99% -2.931e+01 8.788e+02 -0.033 0.9734
stenose70-99% -1.442e+01 6.241e+02 -0.023 0.9816
hsCRP_plasma 1.043e-03 1.179e-03 0.884 0.3766
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 750.18 on 621 degrees of freedom
Residual deviance: 702.43 on 601 degrees of freedom
AIC: 744.43
Number of Fisher Scoring iterations: 13
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_LN ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6R_pg_ug_2015_LN
Trait/outcome.............: Fat10Perc
Effect size...............: 0.198232
Standard error............: 0.077419
Odds ratio (effect size)..: 1.219
Lower 95% CI..............: 1.048
Upper 95% CI..............: 1.419
Z-value...................: 2.560522
P-value...................: 0.01045151
Hosmer and Lemeshow r^2...: 0.063657
Cox and Snell r^2.........: 0.073903
Nagelkerke's pseudo r^2...: 0.10548
Sample size of AE DB......: 2388
Sample size of model......: 622
Missing data %............: 73.9531
- processing IPH
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Gender + SmokerCurrent, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Gendermale SmokerCurrentyes
0.2477 0.1965 0.6427 0.3405
Degrees of Freedom: 619 Total (i.e. Null); 616 Residual
Null Deviance: 835.3
Residual Deviance: 812.2 AIC: 820.2
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + hsCRP_plasma, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.9967 -1.2590 0.7936 0.9869 1.6855
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 8.507e-01 1.890e+00 0.450 0.652583
currentDF[, PROTEIN] 1.700e-01 7.341e-02 2.316 0.020566 *
Age -3.382e-03 1.076e-02 -0.314 0.753214
Gendermale 6.921e-01 1.847e-01 3.748 0.000178 ***
Hypertension.compositeyes -2.837e-01 2.610e-01 -1.087 0.277074
DiabetesStatusDiabetes -1.516e-01 2.132e-01 -0.711 0.477112
SmokerCurrentyes 3.079e-01 1.906e-01 1.616 0.106170
Med.Statin.LLDyes -2.823e-01 2.140e-01 -1.319 0.187174
Med.all.antiplateletyes 3.726e-01 2.944e-01 1.265 0.205711
GFR_MDRD -5.088e-03 4.824e-03 -1.055 0.291601
BMI -6.471e-03 2.342e-02 -0.276 0.782284
CAD_history 1.857e-01 2.002e-01 0.928 0.353513
Stroke_history 1.617e-01 1.891e-01 0.855 0.392598
Peripheral.interv 5.488e-02 2.211e-01 0.248 0.803927
stenose50-70% -2.050e-01 1.476e+00 -0.139 0.889554
stenose70-90% 8.601e-02 1.439e+00 0.060 0.952351
stenose90-99% 2.269e-01 1.441e+00 0.157 0.874921
stenose100% (Occlusion) -3.913e-01 1.763e+00 -0.222 0.824387
stenose50-99% 1.452e+01 6.222e+02 0.023 0.981376
stenose70-99% 1.153e+00 1.848e+00 0.624 0.532790
hsCRP_plasma -5.008e-04 7.982e-04 -0.627 0.530421
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 835.34 on 619 degrees of freedom
Residual deviance: 799.72 on 599 degrees of freedom
AIC: 841.72
Number of Fisher Scoring iterations: 13
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_LN ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6R_pg_ug_2015_LN
Trait/outcome.............: IPH
Effect size...............: 0.170015
Standard error............: 0.073413
Odds ratio (effect size)..: 1.185
Lower 95% CI..............: 1.026
Upper 95% CI..............: 1.369
Z-value...................: 2.315862
P-value...................: 0.0205658
Hosmer and Lemeshow r^2...: 0.042645
Cox and Snell r^2.........: 0.055838
Nagelkerke's pseudo r^2...: 0.07545
Sample size of AE DB......: 2388
Sample size of model......: 620
Missing data %............: 74.03685
Analysis of MCP1_pg_ug_2015_LN.
- processing CalcificationPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + SmokerCurrent + Peripheral.interv + stenose, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Age SmokerCurrentyes Peripheral.interv
-2.91466 -0.34810 0.02251 0.27168 -0.41190
stenose50-70% stenose70-90% stenose90-99% stenose100% (Occlusion) stenose50-99%
0.15264 0.84843 0.97712 1.65836 -14.89486
stenose70-99%
-14.54448
Degrees of Freedom: 637 Total (i.e. Null); 627 Residual
Null Deviance: 882.6
Residual Deviance: 828.1 AIC: 850.1
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + hsCRP_plasma, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.7097 -1.0741 -0.6643 1.1304 1.9192
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -3.674e+00 1.768e+00 -2.078 0.0377 *
currentDF[, PROTEIN] -3.538e-01 6.884e-02 -5.139 2.76e-07 ***
Age 2.253e-02 1.063e-02 2.118 0.0341 *
Gendermale 4.587e-02 1.831e-01 0.250 0.8022
Hypertension.compositeyes 2.489e-01 2.549e-01 0.976 0.3289
DiabetesStatusDiabetes -1.151e-01 2.131e-01 -0.540 0.5890
SmokerCurrentyes 2.944e-01 1.843e-01 1.597 0.1102
Med.Statin.LLDyes -2.974e-01 2.054e-01 -1.448 0.1476
Med.all.antiplateletyes 1.180e-01 2.931e-01 0.403 0.6873
GFR_MDRD 4.104e-03 4.750e-03 0.864 0.3876
BMI 8.952e-03 2.250e-02 0.398 0.6907
CAD_history 2.608e-01 1.947e-01 1.340 0.1803
Stroke_history 1.081e-01 1.845e-01 0.586 0.5580
Peripheral.interv -4.220e-01 2.168e-01 -1.946 0.0516 .
stenose50-70% 1.388e-01 1.300e+00 0.107 0.9150
stenose70-90% 8.620e-01 1.250e+00 0.690 0.4905
stenose90-99% 9.815e-01 1.249e+00 0.786 0.4321
stenose100% (Occlusion) 1.669e+00 1.720e+00 0.970 0.3320
stenose50-99% -1.487e+01 9.913e+02 -0.015 0.9880
stenose70-99% -1.452e+01 6.892e+02 -0.021 0.9832
hsCRP_plasma -1.977e-04 8.180e-04 -0.242 0.8090
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 882.64 on 637 degrees of freedom
Residual deviance: 821.90 on 617 degrees of freedom
AIC: 863.9
Number of Fisher Scoring iterations: 14
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_LN ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_LN
Trait/outcome.............: CalcificationPlaque
Effect size...............: -0.353797
Standard error............: 0.068844
Odds ratio (effect size)..: 0.702
Lower 95% CI..............: 0.613
Upper 95% CI..............: 0.803
Z-value...................: -5.139142
P-value...................: 2.759964e-07
Hosmer and Lemeshow r^2...: 0.068824
Cox and Snell r^2.........: 0.090823
Nagelkerke's pseudo r^2...: 0.121212
Sample size of AE DB......: 2388
Sample size of model......: 638
Missing data %............: 73.28308
- processing CollagenPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
SmokerCurrent + CAD_history, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] SmokerCurrentyes CAD_history
0.7189 -0.2213 0.4743 0.5184
Degrees of Freedom: 640 Total (i.e. Null); 637 Residual
Null Deviance: 683
Residual Deviance: 664.1 AIC: 672.1
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + hsCRP_plasma, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.3057 0.3895 0.6294 0.7514 1.1846
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.277e+01 8.213e+02 0.016 0.98760
currentDF[, PROTEIN] -2.100e-01 8.055e-02 -2.607 0.00914 **
Age 7.341e-03 1.215e-02 0.604 0.54564
Gendermale 1.629e-01 2.112e-01 0.771 0.44070
Hypertension.compositeyes 6.702e-02 2.849e-01 0.235 0.81405
DiabetesStatusDiabetes 1.041e-01 2.531e-01 0.411 0.68101
SmokerCurrentyes 5.453e-01 2.254e-01 2.420 0.01554 *
Med.Statin.LLDyes -7.761e-02 2.380e-01 -0.326 0.74441
Med.all.antiplateletyes 4.811e-01 3.205e-01 1.501 0.13327
GFR_MDRD 2.145e-03 5.486e-03 0.391 0.69574
BMI 3.022e-02 2.743e-02 1.102 0.27053
CAD_history 5.250e-01 2.409e-01 2.179 0.02933 *
Stroke_history 2.599e-01 2.187e-01 1.188 0.23471
Peripheral.interv 1.935e-01 2.606e-01 0.742 0.45781
stenose50-70% -1.407e+01 8.213e+02 -0.017 0.98633
stenose70-90% -1.423e+01 8.213e+02 -0.017 0.98618
stenose90-99% -1.416e+01 8.213e+02 -0.017 0.98625
stenose100% (Occlusion) 1.434e-01 1.082e+03 0.000 0.99989
stenose50-99% -1.897e-01 1.312e+03 0.000 0.99988
stenose70-99% -1.428e+01 8.213e+02 -0.017 0.98613
hsCRP_plasma -7.496e-04 7.473e-04 -1.003 0.31581
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 682.96 on 640 degrees of freedom
Residual deviance: 652.30 on 620 degrees of freedom
AIC: 694.3
Number of Fisher Scoring iterations: 14
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_LN ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_LN
Trait/outcome.............: CollagenPlaque
Effect size...............: -0.209978
Standard error............: 0.080548
Odds ratio (effect size)..: 0.811
Lower 95% CI..............: 0.692
Upper 95% CI..............: 0.949
Z-value...................: -2.606859
P-value...................: 0.009137705
Hosmer and Lemeshow r^2...: 0.044886
Cox and Snell r^2.........: 0.046699
Nagelkerke's pseudo r^2...: 0.071249
Sample size of AE DB......: 2388
Sample size of model......: 641
Missing data %............: 73.15745
- processing Fat10Perc
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Gender + Stroke_history + Peripheral.interv, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Gendermale Stroke_history Peripheral.interv
0.4900 0.1761 0.8581 0.3303 -0.3647
Degrees of Freedom: 640 Total (i.e. Null); 636 Residual
Null Deviance: 779.2
Residual Deviance: 742.6 AIC: 752.6
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + hsCRP_plasma, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.0483 -1.1799 0.6663 0.8380 1.5720
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.446e+01 8.352e+02 0.017 0.9862
currentDF[, PROTEIN] 1.835e-01 7.144e-02 2.569 0.0102 *
Age 6.300e-03 1.139e-02 0.553 0.5802
Gendermale 8.984e-01 1.914e-01 4.695 2.67e-06 ***
Hypertension.compositeyes 1.616e-01 2.721e-01 0.594 0.5527
DiabetesStatusDiabetes -4.434e-02 2.286e-01 -0.194 0.8462
SmokerCurrentyes 7.693e-02 2.003e-01 0.384 0.7009
Med.Statin.LLDyes -7.305e-02 2.281e-01 -0.320 0.7487
Med.all.antiplateletyes 9.405e-02 3.169e-01 0.297 0.7666
GFR_MDRD -1.751e-04 5.212e-03 -0.034 0.9732
BMI 5.013e-03 2.372e-02 0.211 0.8326
CAD_history -1.538e-01 2.096e-01 -0.734 0.4632
Stroke_history 3.187e-01 2.076e-01 1.535 0.1247
Peripheral.interv -3.990e-01 2.240e-01 -1.781 0.0749 .
stenose50-70% -1.511e+01 8.352e+02 -0.018 0.9856
stenose70-90% -1.467e+01 8.352e+02 -0.018 0.9860
stenose90-99% -1.458e+01 8.352e+02 -0.017 0.9861
stenose100% (Occlusion) -1.570e+01 8.352e+02 -0.019 0.9850
stenose50-99% -3.123e+01 1.301e+03 -0.024 0.9808
stenose70-99% -1.572e+01 8.352e+02 -0.019 0.9850
hsCRP_plasma 9.362e-04 1.297e-03 0.722 0.4704
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 779.19 on 640 degrees of freedom
Residual deviance: 729.13 on 620 degrees of freedom
AIC: 771.13
Number of Fisher Scoring iterations: 14
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_LN ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_LN
Trait/outcome.............: Fat10Perc
Effect size...............: 0.183542
Standard error............: 0.071439
Odds ratio (effect size)..: 1.201
Lower 95% CI..............: 1.044
Upper 95% CI..............: 1.382
Z-value...................: 2.569224
P-value...................: 0.01019266
Hosmer and Lemeshow r^2...: 0.064242
Cox and Snell r^2.........: 0.075121
Nagelkerke's pseudo r^2...: 0.106787
Sample size of AE DB......: 2388
Sample size of model......: 641
Missing data %............: 73.15745
- processing IPH
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Gender + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) Gendermale SmokerCurrentyes Med.Statin.LLDyes Med.all.antiplateletyes
-0.2358 0.6317 0.4079 -0.4060 0.4192
Degrees of Freedom: 638 Total (i.e. Null); 634 Residual
Null Deviance: 861.2
Residual Deviance: 838.9 AIC: 848.9
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + hsCRP_plasma, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.9939 -1.2484 0.7941 1.0068 1.5323
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 7.965e-01 1.749e+00 0.455 0.648904
currentDF[, PROTEIN] -4.119e-02 6.612e-02 -0.623 0.533296
Age -4.261e-03 1.057e-02 -0.403 0.686765
Gendermale 6.900e-01 1.822e-01 3.786 0.000153 ***
Hypertension.compositeyes -3.421e-01 2.594e-01 -1.319 0.187182
DiabetesStatusDiabetes -1.849e-01 2.087e-01 -0.886 0.375694
SmokerCurrentyes 3.607e-01 1.870e-01 1.928 0.053803 .
Med.Statin.LLDyes -4.409e-01 2.115e-01 -2.085 0.037101 *
Med.all.antiplateletyes 3.845e-01 2.892e-01 1.329 0.183695
GFR_MDRD -5.883e-03 4.757e-03 -1.237 0.216236
BMI 3.063e-03 2.235e-02 0.137 0.891022
CAD_history 1.915e-01 1.950e-01 0.982 0.325976
Stroke_history 2.583e-01 1.877e-01 1.376 0.168780
Peripheral.interv 1.456e-01 2.168e-01 0.672 0.501897
stenose50-70% -6.460e-01 1.287e+00 -0.502 0.615744
stenose70-90% -2.998e-01 1.246e+00 -0.241 0.809789
stenose90-99% -1.076e-01 1.245e+00 -0.086 0.931122
stenose100% (Occlusion) -8.523e-01 1.608e+00 -0.530 0.596138
stenose50-99% 1.413e+01 6.238e+02 0.023 0.981927
stenose70-99% 5.240e-01 1.702e+00 0.308 0.758158
hsCRP_plasma -5.350e-04 7.957e-04 -0.672 0.501368
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 861.23 on 638 degrees of freedom
Residual deviance: 825.85 on 618 degrees of freedom
AIC: 867.85
Number of Fisher Scoring iterations: 13
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_LN ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_LN
Trait/outcome.............: IPH
Effect size...............: -0.041195
Standard error............: 0.066125
Odds ratio (effect size)..: 0.96
Lower 95% CI..............: 0.843
Upper 95% CI..............: 1.092
Z-value...................: -0.622983
P-value...................: 0.5332958
Hosmer and Lemeshow r^2...: 0.041078
Cox and Snell r^2.........: 0.053859
Nagelkerke's pseudo r^2...: 0.072765
Sample size of AE DB......: 2388
Sample size of model......: 639
Missing data %............: 73.24121 cat("Edit the column names...\n")Edit the column names...colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
"Beta", "s.e.m.",
"OR", "low95CI", "up95CI",
"Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")
cat("Correct the variable types...\n")Correct the variable types...GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)
# Save the data
cat("Writing results to Excel-file...\n")Writing results to Excel-file...### Univariate
write.xlsx(GLM.results,
file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Multi.Protein.PlaquePhenotypes.MODEL4.xlsx"),
row.names = FALSE, col.names = TRUE, sheetName = "Bin.Multi.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")Removing intermediate files...rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)
Inverse-rank normalization
Here we use the inverse-rank normalized data - visually this is more normally distributed.
Analysis of continuous/quantitative plaque traits as a function of serum/plaque IL6(R)/MCP1 levels.
GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")Running linear regression...for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
PROTEIN = TRAITS.PROTEIN.RANK[protein]
cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
for (trait in 1:length(TRAITS.CON.RANK)) {
TRAIT = TRAITS.CON.RANK[trait]
cat(paste0("\n- processing ",TRAIT,"\n\n"))
currentDF <- as.data.frame(AEDB.CEA %>%
dplyr::select(., PROTEIN, TRAIT, COVARIATES_M4) %>%
filter(complete.cases(.))) %>%
filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
# for debug
# print(DT::datatable(currentDF))
# print(nrow(currentDF))
# print(str(currentDF))
### univariate
fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender +
Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose + hsCRP_plasma,
data = currentDF)
model_step <- stepAIC(fit, direction = "both", trace = FALSE)
print(model_step)
print(summary(fit))
GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
GLM.results.TEMP[1,] = GLM.CON(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
GLM.results = rbind(GLM.results, GLM.results.TEMP)
}
}
Analysis of IL6_rank.
- processing Macrophages_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
DiabetesStatus + Med.Statin.LLD + CAD_history, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age DiabetesStatusDiabetes Med.Statin.LLDyes
1.10461 -0.08253 -0.01480 -0.30374 -0.25618
CAD_history
0.25223
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose +
hsCRP_plasma, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1.8179 -0.7134 -0.0181 0.6918 2.5356
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.7610801 1.3489297 2.788 0.00571 **
currentDF[, TRAIT] -0.0850507 0.0553685 -1.536 0.12579
Age -0.0141240 0.0078693 -1.795 0.07389 .
Gendermale -0.0741715 0.1340836 -0.553 0.58064
Hypertension.compositeyes 0.0013148 0.1710049 0.008 0.99387
DiabetesStatusDiabetes -0.2388949 0.1703963 -1.402 0.16216
SmokerCurrentyes 0.0345861 0.1281135 0.270 0.78741
Med.Statin.LLDyes -0.2708574 0.1319532 -2.053 0.04115 *
Med.all.antiplateletyes -0.1694608 0.2416126 -0.701 0.48372
GFR_MDRD 0.0031164 0.0036469 0.855 0.39364
BMI -0.0127276 0.0159841 -0.796 0.42663
CAD_history 0.3022280 0.1347847 2.242 0.02582 *
Stroke_history 0.0493347 0.1319419 0.374 0.70879
Peripheral.interv -0.0289171 0.1476817 -0.196 0.84492
stenose50-70% -2.6729158 1.0737710 -2.489 0.01345 *
stenose70-90% -2.3552407 0.9731508 -2.420 0.01623 *
stenose90-99% -2.4519655 0.9711424 -2.525 0.01220 *
stenose100% (Occlusion) -2.8741799 1.0913091 -2.634 0.00897 **
hsCRP_plasma 0.0004683 0.0006729 0.696 0.48715
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9476 on 249 degrees of freedom
Multiple R-squared: 0.09334, Adjusted R-squared: 0.0278
F-statistic: 1.424 on 18 and 249 DF, p-value: 0.1203
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_rank ' with ' Macrophages_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_rank
Trait/outcome.............: Macrophages_rank
Effect size...............: -0.085051
Standard error............: 0.055368
Odds ratio (effect size)..: 0.918
Lower 95% CI..............: 0.824
Upper 95% CI..............: 1.024
T-value...................: -1.536086
P-value...................: 0.1257871
R^2.......................: 0.093337
Adjusted r^2..............: 0.027795
Sample size of AE DB......: 2388
Sample size of model......: 268
Missing data %............: 88.77722
- processing SMC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Age + DiabetesStatus + Med.Statin.LLD +
CAD_history, data = currentDF)
Coefficients:
(Intercept) Age DiabetesStatusDiabetes Med.Statin.LLDyes CAD_history
1.12857 -0.01497 -0.30012 -0.24553 0.21063
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose +
hsCRP_plasma, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1.9058 -0.6863 -0.0012 0.7162 2.5728
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.7574126 1.3650393 2.753 0.00635 **
currentDF[, TRAIT] 0.0291045 0.0602489 0.483 0.62947
Age -0.0142636 0.0080214 -1.778 0.07659 .
Gendermale -0.0575822 0.1363481 -0.422 0.67316
Hypertension.compositeyes -0.0416310 0.1714500 -0.243 0.80835
DiabetesStatusDiabetes -0.2241496 0.1718816 -1.304 0.19341
SmokerCurrentyes 0.0435941 0.1284336 0.339 0.73457
Med.Statin.LLDyes -0.2565874 0.1344193 -1.909 0.05743 .
Med.all.antiplateletyes -0.1217945 0.2435305 -0.500 0.61743
GFR_MDRD 0.0027549 0.0037036 0.744 0.45767
BMI -0.0163280 0.0161338 -1.012 0.31250
CAD_history 0.2534281 0.1364387 1.857 0.06443 .
Stroke_history 0.0387521 0.1340692 0.289 0.77279
Peripheral.interv 0.0138521 0.1499281 0.092 0.92646
stenose50-70% -2.6358961 1.0930424 -2.412 0.01661 *
stenose70-90% -2.2673760 0.9862332 -2.299 0.02233 *
stenose90-99% -2.3358513 0.9830834 -2.376 0.01826 *
stenose100% (Occlusion) -2.7347521 1.1035886 -2.478 0.01387 *
hsCRP_plasma 0.0005594 0.0006783 0.825 0.41032
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9598 on 249 degrees of freedom
Multiple R-squared: 0.08181, Adjusted R-squared: 0.01543
F-statistic: 1.233 on 18 and 249 DF, p-value: 0.2353
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_rank ' with ' SMC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_rank
Trait/outcome.............: SMC_rank
Effect size...............: 0.029104
Standard error............: 0.060249
Odds ratio (effect size)..: 1.03
Lower 95% CI..............: 0.915
Upper 95% CI..............: 1.159
T-value...................: 0.48307
P-value...................: 0.6294703
R^2.......................: 0.08181
Adjusted r^2..............: 0.015435
Sample size of AE DB......: 2388
Sample size of model......: 268
Missing data %............: 88.77722
- processing VesselDensity_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Age + DiabetesStatus + Med.Statin.LLD +
CAD_history, data = currentDF)
Coefficients:
(Intercept) Age DiabetesStatusDiabetes Med.Statin.LLDyes CAD_history
1.24577 -0.01694 -0.29696 -0.22735 0.20814
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose +
hsCRP_plasma, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1.91883 -0.73434 -0.02321 0.71205 2.41508
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.9391587 1.3789460 2.857 0.00465 **
currentDF[, TRAIT] -0.1104053 0.0755416 -1.462 0.14516
Age -0.0163042 0.0080494 -2.026 0.04390 *
Gendermale -0.0309865 0.1359232 -0.228 0.81986
Hypertension.compositeyes 0.0159954 0.1748906 0.091 0.92720
DiabetesStatusDiabetes -0.2229010 0.1732834 -1.286 0.19954
SmokerCurrentyes 0.0548484 0.1288247 0.426 0.67066
Med.Statin.LLDyes -0.2373739 0.1340207 -1.771 0.07778 .
Med.all.antiplateletyes -0.1495074 0.2468978 -0.606 0.54538
GFR_MDRD 0.0030589 0.0037484 0.816 0.41528
BMI -0.0153285 0.0161300 -0.950 0.34289
CAD_history 0.2531491 0.1360555 1.861 0.06400 .
Stroke_history 0.0566228 0.1338465 0.423 0.67264
Peripheral.interv -0.0107785 0.1500217 -0.072 0.94278
stenose50-70% -2.7403356 1.0878828 -2.519 0.01241 *
stenose70-90% -2.4021458 0.9863600 -2.435 0.01559 *
stenose90-99% -2.4746008 0.9837156 -2.516 0.01253 *
stenose100% (Occlusion) -2.6850845 1.1429843 -2.349 0.01961 *
hsCRP_plasma 0.0006186 0.0006768 0.914 0.36162
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9561 on 244 degrees of freedom
Multiple R-squared: 0.0901, Adjusted R-squared: 0.02298
F-statistic: 1.342 on 18 and 244 DF, p-value: 0.1622
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_rank ' with ' VesselDensity_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_rank
Trait/outcome.............: VesselDensity_rank
Effect size...............: -0.110405
Standard error............: 0.075542
Odds ratio (effect size)..: 0.895
Lower 95% CI..............: 0.772
Upper 95% CI..............: 1.038
T-value...................: -1.461517
P-value...................: 0.1451602
R^2.......................: 0.090105
Adjusted r^2..............: 0.022981
Sample size of AE DB......: 2388
Sample size of model......: 263
Missing data %............: 88.9866
Analysis of MCP1_rank.
- processing Macrophages_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + Med.Statin.LLD + Med.all.antiplatelet,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age Gendermale Hypertension.compositeyes
0.90896 0.09036 -0.01335 0.46652 -0.40919
Med.Statin.LLDyes Med.all.antiplateletyes
-0.28685 0.33829
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose +
hsCRP_plasma, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.64392 -0.75932 0.06352 0.66219 2.52268
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.6665896 1.4253886 1.871 0.06249 .
currentDF[, TRAIT] 0.0725218 0.0582033 1.246 0.21387
Age -0.0182822 0.0082423 -2.218 0.02740 *
Gendermale 0.4529019 0.1398723 3.238 0.00136 **
Hypertension.compositeyes -0.4589879 0.1783022 -2.574 0.01059 *
DiabetesStatusDiabetes -0.2271631 0.1698214 -1.338 0.18216
SmokerCurrentyes -0.0645039 0.1356126 -0.476 0.63472
Med.Statin.LLDyes -0.2770796 0.1404174 -1.973 0.04951 *
Med.all.antiplateletyes 0.2477201 0.2397855 1.033 0.30251
GFR_MDRD -0.0023312 0.0037741 -0.618 0.53731
BMI -0.0058342 0.0158908 -0.367 0.71381
CAD_history 0.1504213 0.1426425 1.055 0.29261
Stroke_history 0.0147719 0.1374410 0.107 0.91449
Peripheral.interv -0.1156181 0.1590149 -0.727 0.46782
stenose50-70% -1.1952274 1.1480343 -1.041 0.29878
stenose70-90% -0.8922860 1.0461758 -0.853 0.39449
stenose90-99% -0.9612964 1.0452526 -0.920 0.35858
stenose100% (Occlusion) -2.0375086 1.2127086 -1.680 0.09412 .
hsCRP_plasma 0.0002384 0.0007237 0.329 0.74215
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.021 on 263 degrees of freedom
Multiple R-squared: 0.1195, Adjusted R-squared: 0.05923
F-statistic: 1.983 on 18 and 263 DF, p-value: 0.01102
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' Macrophages_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: Macrophages_rank
Effect size...............: 0.072522
Standard error............: 0.058203
Odds ratio (effect size)..: 1.075
Lower 95% CI..............: 0.959
Upper 95% CI..............: 1.205
T-value...................: 1.246007
P-value...................: 0.2138706
R^2.......................: 0.119492
Adjusted r^2..............: 0.059229
Sample size of AE DB......: 2388
Sample size of model......: 282
Missing data %............: 88.19096
- processing SMC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + Med.Statin.LLD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age Gendermale Hypertension.compositeyes
1.42056 -0.15786 -0.01593 0.43251 -0.37321
Med.Statin.LLDyes
-0.27687
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose +
hsCRP_plasma, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.62938 -0.73219 0.02213 0.64926 2.37564
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.7696836 1.4071347 1.968 0.05008 .
currentDF[, TRAIT] -0.1596645 0.0608963 -2.622 0.00925 **
Age -0.0204731 0.0082046 -2.495 0.01320 *
Gendermale 0.4070673 0.1387248 2.934 0.00364 **
Hypertension.compositeyes -0.4316998 0.1745410 -2.473 0.01402 *
DiabetesStatusDiabetes -0.1996904 0.1681466 -1.188 0.23606
SmokerCurrentyes -0.0737715 0.1324886 -0.557 0.57813
Med.Statin.LLDyes -0.2893915 0.1398711 -2.069 0.03952 *
Med.all.antiplateletyes 0.2047848 0.2362858 0.867 0.38691
GFR_MDRD -0.0015786 0.0037332 -0.423 0.67274
BMI -0.0026959 0.0157030 -0.172 0.86382
CAD_history 0.2159653 0.1407712 1.534 0.12619
Stroke_history 0.0502527 0.1362064 0.369 0.71247
Peripheral.interv -0.1498806 0.1578178 -0.950 0.34313
stenose50-70% -1.1018731 1.1377402 -0.968 0.33370
stenose70-90% -0.9048915 1.0354045 -0.874 0.38294
stenose90-99% -0.9856399 1.0334683 -0.954 0.34110
stenose100% (Occlusion) -2.1331283 1.1977774 -1.781 0.07608 .
hsCRP_plasma 0.0001592 0.0007124 0.223 0.82333
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.01 on 263 degrees of freedom
Multiple R-squared: 0.1323, Adjusted R-squared: 0.0729
F-statistic: 2.227 on 18 and 263 DF, p-value: 0.003394
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' SMC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: SMC_rank
Effect size...............: -0.159664
Standard error............: 0.060896
Odds ratio (effect size)..: 0.852
Lower 95% CI..............: 0.757
Upper 95% CI..............: 0.96
T-value...................: -2.621908
P-value...................: 0.009253515
R^2.......................: 0.132285
Adjusted r^2..............: 0.072898
Sample size of AE DB......: 2388
Sample size of model......: 282
Missing data %............: 88.19096
- processing VesselDensity_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Age + Gender + Hypertension.composite +
Med.Statin.LLD, data = currentDF)
Coefficients:
(Intercept) Age Gendermale Hypertension.compositeyes Med.Statin.LLDyes
1.22370 -0.01415 0.48743 -0.34890 -0.28148
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose +
hsCRP_plasma, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.65420 -0.71370 0.04394 0.68616 2.62817
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.7729405 1.4437166 1.921 0.05587 .
currentDF[, TRAIT] -0.0892692 0.0772661 -1.155 0.24901
Age -0.0187057 0.0083515 -2.240 0.02595 *
Gendermale 0.4637153 0.1414187 3.279 0.00118 **
Hypertension.compositeyes -0.4013863 0.1791037 -2.241 0.02587 *
DiabetesStatusDiabetes -0.2122018 0.1717052 -1.236 0.21763
SmokerCurrentyes -0.0928692 0.1350210 -0.688 0.49219
Med.Statin.LLDyes -0.2859631 0.1419745 -2.014 0.04502 *
Med.all.antiplateletyes 0.2033344 0.2404618 0.846 0.39856
GFR_MDRD -0.0015604 0.0038453 -0.406 0.68522
BMI -0.0042388 0.0159383 -0.266 0.79049
CAD_history 0.1856238 0.1430297 1.298 0.19551
Stroke_history 0.0175814 0.1381435 0.127 0.89883
Peripheral.interv -0.1036751 0.1606087 -0.646 0.51917
stenose50-70% -1.3545938 1.1546015 -1.173 0.24179
stenose70-90% -1.0398848 1.0539217 -0.987 0.32472
stenose90-99% -1.1278478 1.0514119 -1.073 0.28440
stenose100% (Occlusion) -2.2387467 1.2187238 -1.837 0.06736 .
hsCRP_plasma 0.0002015 0.0007230 0.279 0.78068
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.023 on 259 degrees of freedom
Multiple R-squared: 0.1172, Adjusted R-squared: 0.05581
F-statistic: 1.91 on 18 and 259 DF, p-value: 0.01554
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' VesselDensity_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: VesselDensity_rank
Effect size...............: -0.089269
Standard error............: 0.077266
Odds ratio (effect size)..: 0.915
Lower 95% CI..............: 0.786
Upper 95% CI..............: 1.064
T-value...................: -1.155347
P-value...................: 0.2490129
R^2.......................: 0.117163
Adjusted r^2..............: 0.055807
Sample size of AE DB......: 2388
Sample size of model......: 278
Missing data %............: 88.35846
Analysis of IL6_pg_ug_2015_rank.
- processing Macrophages_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + CAD_history +
Stroke_history + hsCRP_plasma, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] CAD_history Stroke_history hsCRP_plasma
-0.0102133 0.1194738 -0.2522780 0.2547525 0.0004843
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose +
hsCRP_plasma, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.08559 -0.64622 0.01259 0.61647 2.89440
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.3292152 0.8037644 0.410 0.68225
currentDF[, TRAIT] 0.1188011 0.0390716 3.041 0.00247 **
Age -0.0057304 0.0049456 -1.159 0.24705
Gendermale 0.0779960 0.0863697 0.903 0.36687
Hypertension.compositeyes 0.0059062 0.1196948 0.049 0.96066
DiabetesStatusDiabetes 0.0265491 0.0992727 0.267 0.78923
SmokerCurrentyes 0.0556969 0.0867398 0.642 0.52105
Med.Statin.LLDyes -0.1269603 0.0951680 -1.334 0.18269
Med.all.antiplateletyes 0.0842321 0.1376219 0.612 0.54074
GFR_MDRD -0.0021476 0.0022014 -0.976 0.32968
BMI -0.0108513 0.0105771 -1.026 0.30535
CAD_history -0.2196432 0.0916106 -2.398 0.01681 *
Stroke_history 0.2495314 0.0858403 2.907 0.00379 **
Peripheral.interv -0.0319998 0.1011919 -0.316 0.75194
stenose50-70% 0.3367012 0.5829363 0.578 0.56376
stenose70-90% 0.5050711 0.5620595 0.899 0.36923
stenose90-99% 0.3553822 0.5620349 0.632 0.52743
stenose100% (Occlusion) 0.5893787 0.7441795 0.792 0.42869
stenose70-99% 1.0155702 0.7896134 1.286 0.19889
hsCRP_plasma 0.0004197 0.0003397 1.235 0.21717
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9632 on 593 degrees of freedom
Multiple R-squared: 0.06585, Adjusted R-squared: 0.03592
F-statistic: 2.2 on 19 and 593 DF, p-value: 0.002438
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_rank ' with ' Macrophages_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_pg_ug_2015_rank
Trait/outcome.............: Macrophages_rank
Effect size...............: 0.118801
Standard error............: 0.039072
Odds ratio (effect size)..: 1.126
Lower 95% CI..............: 1.043
Upper 95% CI..............: 1.216
T-value...................: 3.040602
P-value...................: 0.00246525
R^2.......................: 0.065849
Adjusted r^2..............: 0.035918
Sample size of AE DB......: 2388
Sample size of model......: 613
Missing data %............: 74.32998
- processing SMC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Med.Statin.LLD + CAD_history + Stroke_history, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age Med.Statin.LLDyes CAD_history Stroke_history
0.631954 -0.141333 -0.008044 -0.148043 -0.179849 0.282466
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose +
hsCRP_plasma, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.89600 -0.62467 0.04063 0.64149 2.69206
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.3981963 0.8034180 0.496 0.62034
currentDF[, TRAIT] -0.1324395 0.0412655 -3.209 0.00140 **
Age -0.0087393 0.0049756 -1.756 0.07953 .
Gendermale 0.0608222 0.0871623 0.698 0.48557
Hypertension.compositeyes 0.0015723 0.1196609 0.013 0.98952
DiabetesStatusDiabetes 0.0401908 0.0993075 0.405 0.68584
SmokerCurrentyes 0.0403300 0.0867586 0.465 0.64221
Med.Statin.LLDyes -0.1444999 0.0955452 -1.512 0.13097
Med.all.antiplateletyes 0.0672193 0.1376200 0.488 0.62542
GFR_MDRD -0.0014576 0.0022057 -0.661 0.50898
BMI -0.0077850 0.0106146 -0.733 0.46360
CAD_history -0.1880533 0.0919104 -2.046 0.04119 *
Stroke_history 0.2641847 0.0858106 3.079 0.00218 **
Peripheral.interv -0.0499322 0.1018471 -0.490 0.62413
stenose50-70% 0.3351941 0.5826500 0.575 0.56531
stenose70-90% 0.5366193 0.5620986 0.955 0.34013
stenose90-99% 0.4179846 0.5624100 0.743 0.45765
stenose100% (Occlusion) 0.6346060 0.7444406 0.852 0.39430
stenose70-99% 1.0566801 0.7893180 1.339 0.18117
hsCRP_plasma 0.0003459 0.0003393 1.020 0.30833
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9626 on 591 degrees of freedom
Multiple R-squared: 0.06775, Adjusted R-squared: 0.03778
F-statistic: 2.261 on 19 and 591 DF, p-value: 0.001744
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_rank ' with ' SMC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_pg_ug_2015_rank
Trait/outcome.............: SMC_rank
Effect size...............: -0.13244
Standard error............: 0.041265
Odds ratio (effect size)..: 0.876
Lower 95% CI..............: 0.808
Upper 95% CI..............: 0.95
T-value...................: -3.209452
P-value...................: 0.001401953
R^2.......................: 0.06775
Adjusted r^2..............: 0.037779
Sample size of AE DB......: 2388
Sample size of model......: 611
Missing data %............: 74.41374
- processing VesselDensity_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Med.Statin.LLD +
CAD_history + Stroke_history, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Med.Statin.LLDyes CAD_history Stroke_history
0.06268 -0.05999 -0.15096 -0.22254 0.29840
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose +
hsCRP_plasma, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.02020 -0.58612 0.03226 0.62982 2.78365
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.4682123 0.8229269 0.569 0.56961
currentDF[, TRAIT] -0.0586902 0.0423583 -1.386 0.16644
Age -0.0059465 0.0051230 -1.161 0.24624
Gendermale 0.0795257 0.0891399 0.892 0.37270
Hypertension.compositeyes -0.0106395 0.1234224 -0.086 0.93134
DiabetesStatusDiabetes 0.0148287 0.1048173 0.141 0.88755
SmokerCurrentyes 0.0750944 0.0906607 0.828 0.40786
Med.Statin.LLDyes -0.1554293 0.0982137 -1.583 0.11409
Med.all.antiplateletyes 0.0491141 0.1460104 0.336 0.73672
GFR_MDRD -0.0025222 0.0023388 -1.078 0.28130
BMI -0.0087790 0.0109157 -0.804 0.42159
CAD_history -0.2018037 0.0954170 -2.115 0.03488 *
Stroke_history 0.2799083 0.0898376 3.116 0.00193 **
Peripheral.interv -0.0524032 0.1069737 -0.490 0.62442
stenose50-70% 0.1592612 0.5933329 0.268 0.78848
stenose70-90% 0.3658739 0.5687231 0.643 0.52028
stenose90-99% 0.2478824 0.5680324 0.436 0.66273
stenose100% (Occlusion) 0.4433709 0.7506941 0.591 0.55502
stenose70-99% 1.9707718 1.1255726 1.751 0.08051 .
hsCRP_plasma 0.0003700 0.0005774 0.641 0.52192
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9695 on 555 degrees of freedom
Multiple R-squared: 0.06092, Adjusted R-squared: 0.02877
F-statistic: 1.895 on 19 and 555 DF, p-value: 0.01246
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_rank ' with ' VesselDensity_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_pg_ug_2015_rank
Trait/outcome.............: VesselDensity_rank
Effect size...............: -0.05869
Standard error............: 0.042358
Odds ratio (effect size)..: 0.943
Lower 95% CI..............: 0.868
Upper 95% CI..............: 1.025
T-value...................: -1.385565
P-value...................: 0.1664362
R^2.......................: 0.060915
Adjusted r^2..............: 0.028766
Sample size of AE DB......: 2388
Sample size of model......: 575
Missing data %............: 75.92127
Analysis of IL6R_pg_ug_2015_rank.
- processing Macrophages_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
DiabetesStatus + Med.Statin.LLD + GFR_MDRD + Stroke_history +
Peripheral.interv + stenose, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age DiabetesStatusDiabetes Med.Statin.LLDyes
0.435203 0.178335 -0.010371 -0.167664 -0.438824
GFR_MDRD Stroke_history Peripheral.interv stenose50-70% stenose70-90%
-0.003929 0.177631 0.251044 0.754456 0.775869
stenose90-99% stenose100% (Occlusion) stenose50-99% stenose70-99%
1.023291 0.513541 0.658657 -0.199445
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose +
hsCRP_plasma, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.2897 -0.6551 -0.0386 0.6427 3.1361
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.5614134 0.9022961 0.622 0.5340
currentDF[, TRAIT] 0.1796870 0.0394568 4.554 6.38e-06 ***
Age -0.0092173 0.0050214 -1.836 0.0669 .
Gendermale 0.0124573 0.0872508 0.143 0.8865
Hypertension.compositeyes -0.0178822 0.1207259 -0.148 0.8823
DiabetesStatusDiabetes -0.1532610 0.1003493 -1.527 0.1272
SmokerCurrentyes 0.0806171 0.0878219 0.918 0.3590
Med.Statin.LLDyes -0.4205752 0.0971165 -4.331 1.74e-05 ***
Med.all.antiplateletyes -0.0097451 0.1398121 -0.070 0.9445
GFR_MDRD -0.0044086 0.0022316 -1.976 0.0487 *
BMI -0.0088807 0.0110444 -0.804 0.4217
CAD_history -0.0871286 0.0931680 -0.935 0.3501
Stroke_history 0.1557898 0.0868635 1.794 0.0734 .
Peripheral.interv 0.2597093 0.1026991 2.529 0.0117 *
stenose50-70% 0.8330438 0.7172129 1.162 0.2459
stenose70-90% 0.8416904 0.6996858 1.203 0.2295
stenose90-99% 1.0877944 0.6999532 1.554 0.1207
stenose100% (Occlusion) 0.5369462 0.8565711 0.627 0.5310
stenose50-99% 0.6525262 0.9835743 0.663 0.5073
stenose70-99% -0.1579257 0.8980699 -0.176 0.8605
hsCRP_plasma -0.0002230 0.0003455 -0.646 0.5188
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9796 on 597 degrees of freedom
Multiple R-squared: 0.1234, Adjusted R-squared: 0.09405
F-statistic: 4.203 on 20 and 597 DF, p-value: 4.233e-09
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_rank ' with ' Macrophages_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6R_pg_ug_2015_rank
Trait/outcome.............: Macrophages_rank
Effect size...............: 0.179687
Standard error............: 0.039457
Odds ratio (effect size)..: 1.197
Lower 95% CI..............: 1.108
Upper 95% CI..............: 1.293
T-value...................: 4.554018
P-value...................: 6.382359e-06
R^2.......................: 0.123416
Adjusted r^2..............: 0.09405
Sample size of AE DB......: 2388
Sample size of model......: 618
Missing data %............: 74.1206
- processing SMC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Age + DiabetesStatus + Med.Statin.LLD +
GFR_MDRD + Stroke_history + Peripheral.interv + stenose,
data = currentDF)
Coefficients:
(Intercept) Age DiabetesStatusDiabetes Med.Statin.LLDyes GFR_MDRD
0.393940 -0.011146 -0.162148 -0.442817 -0.003293
Stroke_history Peripheral.interv stenose50-70% stenose70-90% stenose90-99%
0.190041 0.229073 0.763493 0.810713 1.071875
stenose100% (Occlusion) stenose50-99% stenose70-99%
0.495589 0.607365 -0.133855
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose +
hsCRP_plasma, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.4555 -0.6554 -0.0491 0.6560 3.2215
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.5232714 0.9158052 0.571 0.5680
currentDF[, TRAIT] 0.0628962 0.0426381 1.475 0.1407
Age -0.0097021 0.0051290 -1.892 0.0590 .
Gendermale 0.0723512 0.0895023 0.808 0.4192
Hypertension.compositeyes -0.0058188 0.1225313 -0.047 0.9621
DiabetesStatusDiabetes -0.1604094 0.1019520 -1.573 0.1162
SmokerCurrentyes 0.0572906 0.0892080 0.642 0.5210
Med.Statin.LLDyes -0.4196048 0.0990136 -4.238 2.62e-05 ***
Med.all.antiplateletyes -0.0179751 0.1419354 -0.127 0.8993
GFR_MDRD -0.0039746 0.0022695 -1.751 0.0804 .
BMI -0.0080950 0.0112566 -0.719 0.4723
CAD_history -0.0893468 0.0948629 -0.942 0.3467
Stroke_history 0.1755604 0.0882045 1.990 0.0470 *
Peripheral.interv 0.2385749 0.1049558 2.273 0.0234 *
stenose50-70% 0.7835914 0.7282810 1.076 0.2824
stenose70-90% 0.8101935 0.7107123 1.140 0.2548
stenose90-99% 1.0612696 0.7112906 1.492 0.1362
stenose100% (Occlusion) 0.4137413 0.8706729 0.475 0.6348
stenose50-99% 0.5881169 0.9981839 0.589 0.5560
stenose70-99% -0.1414861 0.9116295 -0.155 0.8767
hsCRP_plasma -0.0002785 0.0003504 -0.795 0.4270
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.994 on 595 degrees of freedom
Multiple R-squared: 0.09475, Adjusted R-squared: 0.06433
F-statistic: 3.114 on 20 and 595 DF, p-value: 6.831e-06
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_rank ' with ' SMC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6R_pg_ug_2015_rank
Trait/outcome.............: SMC_rank
Effect size...............: 0.062896
Standard error............: 0.042638
Odds ratio (effect size)..: 1.065
Lower 95% CI..............: 0.98
Upper 95% CI..............: 1.158
T-value...................: 1.475115
P-value...................: 0.1407105
R^2.......................: 0.094754
Adjusted r^2..............: 0.064325
Sample size of AE DB......: 2388
Sample size of model......: 616
Missing data %............: 74.20436
- processing VesselDensity_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
DiabetesStatus + Med.Statin.LLD + GFR_MDRD + Stroke_history +
Peripheral.interv + stenose, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age DiabetesStatusDiabetes Med.Statin.LLDyes
0.455034 0.091728 -0.012686 -0.203464 -0.448500
GFR_MDRD Stroke_history Peripheral.interv stenose50-70% stenose70-90%
-0.003803 0.205875 0.195164 0.913705 0.922682
stenose90-99% stenose100% (Occlusion) stenose50-99% stenose70-99%
1.166042 0.558003 0.607138 -0.219689
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose +
hsCRP_plasma, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.2856 -0.6402 -0.0580 0.6591 3.1786
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.857e-01 9.328e-01 0.628 0.5303
currentDF[, TRAIT] 9.128e-02 4.377e-02 2.085 0.0375 *
Age -1.185e-02 5.286e-03 -2.241 0.0254 *
Gendermale 3.067e-02 9.141e-02 0.336 0.7374
Hypertension.compositeyes -8.534e-03 1.266e-01 -0.067 0.9463
DiabetesStatusDiabetes -1.918e-01 1.072e-01 -1.790 0.0739 .
SmokerCurrentyes 4.729e-02 9.314e-02 0.508 0.6119
Med.Statin.LLDyes -4.212e-01 1.017e-01 -4.142 3.97e-05 ***
Med.all.antiplateletyes -6.688e-02 1.502e-01 -0.445 0.6563
GFR_MDRD -4.269e-03 2.405e-03 -1.775 0.0764 .
BMI -5.803e-03 1.167e-02 -0.497 0.6192
CAD_history -1.165e-01 9.825e-02 -1.186 0.2362
Stroke_history 1.864e-01 9.218e-02 2.022 0.0437 *
Peripheral.interv 2.121e-01 1.098e-01 1.932 0.0539 .
stenose50-70% 9.716e-01 7.367e-01 1.319 0.1878
stenose70-90% 9.611e-01 7.151e-01 1.344 0.1795
stenose90-99% 1.208e+00 7.149e-01 1.690 0.0915 .
stenose100% (Occlusion) 5.454e-01 8.744e-01 0.624 0.5331
stenose50-99% 6.006e-01 1.002e+00 0.599 0.5493
stenose70-99% -1.766e-01 1.232e+00 -0.143 0.8860
hsCRP_plasma -3.377e-06 5.948e-04 -0.006 0.9955
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.998 on 557 degrees of freedom
Multiple R-squared: 0.09857, Adjusted R-squared: 0.0662
F-statistic: 3.045 on 20 and 557 DF, p-value: 1.121e-05
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_rank ' with ' VesselDensity_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6R_pg_ug_2015_rank
Trait/outcome.............: VesselDensity_rank
Effect size...............: 0.091278
Standard error............: 0.043771
Odds ratio (effect size)..: 1.096
Lower 95% CI..............: 1.006
Upper 95% CI..............: 1.194
T-value...................: 2.085347
P-value...................: 0.03749243
R^2.......................: 0.098566
Adjusted r^2..............: 0.066198
Sample size of AE DB......: 2388
Sample size of model......: 578
Missing data %............: 75.79564
Analysis of MCP1_pg_ug_2015_rank.
- processing Macrophages_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Med.Statin.LLD +
Stroke_history + stenose, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Med.Statin.LLDyes Stroke_history stenose50-70%
-0.42576 -0.06444 -0.22141 0.22349 0.75123
stenose70-90% stenose90-99% stenose100% (Occlusion) stenose50-99% stenose70-99%
0.70819 0.49049 -0.41548 0.49364 1.08929
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose +
hsCRP_plasma, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.10369 -0.69224 -0.04003 0.63048 3.03947
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.5630821 0.8283966 0.680 0.4969
currentDF[, TRAIT] -0.0677480 0.0395711 -1.712 0.0874 .
Age -0.0059183 0.0050491 -1.172 0.2416
Gendermale 0.1226563 0.0875344 1.401 0.1616
Hypertension.compositeyes -0.1546531 0.1216133 -1.272 0.2040
DiabetesStatusDiabetes -0.1084350 0.1004437 -1.080 0.2808
SmokerCurrentyes -0.1235931 0.0878488 -1.407 0.1600
Med.Statin.LLDyes -0.2000183 0.0978361 -2.044 0.0413 *
Med.all.antiplateletyes -0.0576239 0.1401295 -0.411 0.6811
GFR_MDRD -0.0022978 0.0022513 -1.021 0.3078
BMI -0.0091489 0.0107512 -0.851 0.3951
CAD_history -0.0672097 0.0927196 -0.725 0.4688
Stroke_history 0.2190806 0.0876801 2.499 0.0127 *
Peripheral.interv 0.0468415 0.1027657 0.456 0.6487
stenose50-70% 0.7003120 0.6042077 1.159 0.2469
stenose70-90% 0.6869730 0.5825308 1.179 0.2387
stenose90-99% 0.4732416 0.5825709 0.812 0.4169
stenose100% (Occlusion) -0.5522528 0.7712670 -0.716 0.4742
stenose50-99% 0.6623472 0.9166165 0.723 0.4702
stenose70-99% 1.1552669 0.8186149 1.411 0.1587
hsCRP_plasma 0.0004701 0.0003520 1.336 0.1822
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9987 on 616 degrees of freedom
Multiple R-squared: 0.06066, Adjusted R-squared: 0.03016
F-statistic: 1.989 on 20 and 616 DF, p-value: 0.006473
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' Macrophages_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: Macrophages_rank
Effect size...............: -0.067748
Standard error............: 0.039571
Odds ratio (effect size)..: 0.934
Lower 95% CI..............: 0.865
Upper 95% CI..............: 1.01
T-value...................: -1.712058
P-value...................: 0.08738937
R^2.......................: 0.060655
Adjusted r^2..............: 0.030157
Sample size of AE DB......: 2388
Sample size of model......: 637
Missing data %............: 73.32496
- processing SMC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Med.Statin.LLD +
Stroke_history + stenose, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Med.Statin.LLDyes Stroke_history stenose50-70%
-0.5003 -0.1230 -0.2297 0.2087 0.8328
stenose70-90% stenose90-99% stenose100% (Occlusion) stenose50-99% stenose70-99%
0.7985 0.5948 -0.2635 0.5650 1.1288
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose +
hsCRP_plasma, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.88484 -0.70661 -0.03428 0.63432 3.07566
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.5742271 0.8260859 0.695 0.48724
currentDF[, TRAIT] -0.1189861 0.0415453 -2.864 0.00433 **
Age -0.0070845 0.0050597 -1.400 0.16197
Gendermale 0.0721309 0.0882979 0.817 0.41430
Hypertension.compositeyes -0.1591920 0.1212731 -1.313 0.18978
DiabetesStatusDiabetes -0.0980149 0.1002494 -0.978 0.32860
SmokerCurrentyes -0.1196738 0.0876529 -1.365 0.17265
Med.Statin.LLDyes -0.2126537 0.0979115 -2.172 0.03025 *
Med.all.antiplateletyes -0.0655511 0.1398324 -0.469 0.63939
GFR_MDRD -0.0021616 0.0022506 -0.960 0.33720
BMI -0.0079822 0.0107586 -0.742 0.45841
CAD_history -0.0566264 0.0926877 -0.611 0.54147
Stroke_history 0.2087343 0.0874892 2.386 0.01734 *
Peripheral.interv 0.0554092 0.1031622 0.537 0.59139
stenose50-70% 0.7817065 0.6025178 1.297 0.19498
stenose70-90% 0.7713051 0.5811929 1.327 0.18497
stenose90-99% 0.5692721 0.5816193 0.979 0.32808
stenose100% (Occlusion) -0.3952595 0.7697019 -0.514 0.60777
stenose50-99% 0.7193582 0.9137138 0.787 0.43142
stenose70-99% 1.2093888 0.8164214 1.481 0.13903
hsCRP_plasma 0.0004756 0.0003507 1.356 0.17556
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9958 on 614 degrees of freedom
Multiple R-squared: 0.06904, Adjusted R-squared: 0.03871
F-statistic: 2.277 on 20 and 614 DF, p-value: 0.001266
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' SMC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: SMC_rank
Effect size...............: -0.118986
Standard error............: 0.041545
Odds ratio (effect size)..: 0.888
Lower 95% CI..............: 0.818
Upper 95% CI..............: 0.963
T-value...................: -2.864007
P-value...................: 0.004326106
R^2.......................: 0.069035
Adjusted r^2..............: 0.038711
Sample size of AE DB......: 2388
Sample size of model......: 635
Missing data %............: 73.40871
- processing VesselDensity_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Med.Statin.LLD +
Stroke_history, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Med.Statin.LLDyes Stroke_history
0.1628 -0.1192 -0.2413 0.2636
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose +
hsCRP_plasma, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.96854 -0.64904 -0.03876 0.60683 3.06536
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.6933010 0.8499220 0.816 0.41500
currentDF[, TRAIT] -0.0966390 0.0431813 -2.238 0.02560 *
Age -0.0061171 0.0052516 -1.165 0.24458
Gendermale 0.1368620 0.0907245 1.509 0.13196
Hypertension.compositeyes -0.1448775 0.1262510 -1.148 0.25164
DiabetesStatusDiabetes -0.1093450 0.1061710 -1.030 0.30349
SmokerCurrentyes -0.1075266 0.0920646 -1.168 0.24331
Med.Statin.LLDyes -0.2274730 0.1013434 -2.245 0.02517 *
Med.all.antiplateletyes -0.0498487 0.1486438 -0.335 0.73748
GFR_MDRD -0.0029751 0.0023967 -1.241 0.21499
BMI -0.0085743 0.0112184 -0.764 0.44500
CAD_history -0.0655035 0.0966036 -0.678 0.49800
Stroke_history 0.2537293 0.0920522 2.756 0.00603 **
Peripheral.interv 0.0148010 0.1086944 0.136 0.89173
stenose50-70% 0.5323579 0.6160904 0.864 0.38790
stenose70-90% 0.5842951 0.5905842 0.989 0.32291
stenose90-99% 0.3761791 0.5899430 0.638 0.52395
stenose100% (Occlusion) -0.6197206 0.7795402 -0.795 0.42695
stenose50-99% 0.7112581 0.9245184 0.769 0.44201
stenose70-99% 1.1103070 1.1692152 0.950 0.34270
hsCRP_plasma -0.0001038 0.0005997 -0.173 0.86270
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.007 on 576 degrees of freedom
Multiple R-squared: 0.06809, Adjusted R-squared: 0.03573
F-statistic: 2.104 on 20 and 576 DF, p-value: 0.003462
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' VesselDensity_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: VesselDensity_rank
Effect size...............: -0.096639
Standard error............: 0.043181
Odds ratio (effect size)..: 0.908
Lower 95% CI..............: 0.834
Upper 95% CI..............: 0.988
T-value...................: -2.237981
P-value...................: 0.02560367
R^2.......................: 0.068089
Adjusted r^2..............: 0.035731
Sample size of AE DB......: 2388
Sample size of model......: 597
Missing data %............: 75 cat("Edit the column names...\n")Edit the column names...colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
"Beta", "s.e.m.",
"OR", "low95CI", "up95CI",
"T-value", "P-value", "r^2", "r^2_adj", "AE_N", "Model_N", "Perc_Miss")
cat("Correct the variable types...\n")Correct the variable types...GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)
# Save the data
cat("Writing results to Excel-file...\n")Writing results to Excel-file...### Univariate
library(openxlsx)
write.xlsx(GLM.results,
file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Con.Multi.Protein.PlaquePhenotypes.RANK.MODEL4.xlsx"),
row.names = FALSE, col.names = TRUE, sheetName = "Con.Multi.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")Removing intermediate files...rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)
Analysis of binary plaque traits as a function of serum/plaque IL6(R)/MCP1 levels.
GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
PROTEIN = TRAITS.PROTEIN.RANK[protein]
cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
for (trait in 1:length(TRAITS.BIN)) {
TRAIT = TRAITS.BIN[trait]
cat(paste0("\n- processing ",TRAIT,"\n\n"))
currentDF <- as.data.frame(AEDB.CEA %>%
dplyr::select(., PROTEIN, TRAIT, COVARIATES_M4) %>%
filter(complete.cases(.))) %>%
filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
# for debug
# print(DT::datatable(currentDF))
# print(nrow(currentDF))
# print(str(currentDF))
# print(class(currentDF[,TRAIT]))
### univariate
fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender +
Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose + hsCRP_plasma,
data = currentDF, family = binomial(link = "logit"))
model_step <- stepAIC(fit, direction = "both", trace = FALSE)
print(model_step)
print(summary(fit))
GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
GLM.results = rbind(GLM.results, GLM.results.TEMP)
}
}
Analysis of IL6_rank.
- processing CalcificationPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Hypertension.composite +
Med.all.antiplatelet + Peripheral.interv, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) Hypertension.compositeyes Med.all.antiplateletyes Peripheral.interv
-1.1596 0.6403 1.0824 -0.5324
Degrees of Freedom: 268 Total (i.e. Null); 265 Residual
Null Deviance: 367.8
Residual Deviance: 357.1 AIC: 365.1
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + hsCRP_plasma, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.721 -1.207 0.777 1.024 1.987
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -1.657e+01 8.827e+02 -0.019 0.9850
currentDF[, PROTEIN] 1.193e-01 1.379e-01 0.865 0.3868
Age 6.003e-03 1.744e-02 0.344 0.7307
Gendermale 2.131e-02 2.932e-01 0.073 0.9421
Hypertension.compositeyes 7.854e-01 3.754e-01 2.092 0.0364 *
DiabetesStatusDiabetes -4.366e-01 3.729e-01 -1.171 0.2417
SmokerCurrentyes -9.264e-02 2.784e-01 -0.333 0.7393
Med.Statin.LLDyes 7.990e-02 2.926e-01 0.273 0.7848
Med.all.antiplateletyes 1.452e+00 5.763e-01 2.519 0.0118 *
GFR_MDRD -4.875e-03 8.062e-03 -0.605 0.5454
BMI -3.826e-03 3.503e-02 -0.109 0.9130
CAD_history 1.639e-01 2.999e-01 0.547 0.5847
Stroke_history -4.972e-02 2.916e-01 -0.171 0.8646
Peripheral.interv -5.325e-01 3.199e-01 -1.665 0.0959 .
stenose50-70% 1.555e+01 8.827e+02 0.018 0.9859
stenose70-90% 1.533e+01 8.827e+02 0.017 0.9861
stenose90-99% 1.478e+01 8.827e+02 0.017 0.9866
stenose100% (Occlusion) 1.670e+01 8.827e+02 0.019 0.9849
hsCRP_plasma 2.293e-04 1.500e-03 0.153 0.8785
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 367.81 on 268 degrees of freedom
Residual deviance: 347.27 on 250 degrees of freedom
AIC: 385.27
Number of Fisher Scoring iterations: 13
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_rank ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_rank
Trait/outcome.............: CalcificationPlaque
Effect size...............: 0.119317
Standard error............: 0.137871
Odds ratio (effect size)..: 1.127
Lower 95% CI..............: 0.86
Upper 95% CI..............: 1.476
Z-value...................: 0.865425
P-value...................: 0.3868057
Hosmer and Lemeshow r^2...: 0.055845
Cox and Snell r^2.........: 0.073515
Nagelkerke's pseudo r^2...: 0.09865
Sample size of AE DB......: 2388
Sample size of model......: 269
Missing data %............: 88.73534
- processing CollagenPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ 1, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept)
1.265
Degrees of Freedom: 267 Total (i.e. Null); 267 Residual
Null Deviance: 282.5
Residual Deviance: 282.5 AIC: 284.5
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + hsCRP_plasma, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.03216 0.00043 0.63109 0.74468 1.15631
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.732e+01 2.400e+03 0.007 0.994
currentDF[, PROTEIN] -1.979e-01 1.619e-01 -1.223 0.221
Age 1.704e-03 2.059e-02 0.083 0.934
Gendermale -2.853e-01 3.592e-01 -0.794 0.427
Hypertension.compositeyes 1.133e-01 4.443e-01 0.255 0.799
DiabetesStatusDiabetes 9.509e-02 4.506e-01 0.211 0.833
SmokerCurrentyes 3.723e-01 3.388e-01 1.099 0.272
Med.Statin.LLDyes 5.238e-03 3.450e-01 0.015 0.988
Med.all.antiplateletyes 9.172e-01 5.669e-01 1.618 0.106
GFR_MDRD -2.450e-03 9.592e-03 -0.255 0.798
BMI -3.337e-02 4.034e-02 -0.827 0.408
CAD_history 1.347e-01 3.525e-01 0.382 0.702
Stroke_history 2.922e-01 3.577e-01 0.817 0.414
Peripheral.interv -4.212e-01 3.606e-01 -1.168 0.243
stenose50-70% -9.056e-01 2.618e+03 0.000 1.000
stenose70-90% -1.587e+01 2.400e+03 -0.007 0.995
stenose90-99% -1.618e+01 2.400e+03 -0.007 0.995
stenose100% (Occlusion) -3.871e-01 2.662e+03 0.000 1.000
hsCRP_plasma 2.233e-05 1.768e-03 0.013 0.990
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 282.52 on 267 degrees of freedom
Residual deviance: 268.34 on 249 degrees of freedom
AIC: 306.34
Number of Fisher Scoring iterations: 15
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_rank ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_rank
Trait/outcome.............: CollagenPlaque
Effect size...............: -0.197947
Standard error............: 0.161913
Odds ratio (effect size)..: 0.82
Lower 95% CI..............: 0.597
Upper 95% CI..............: 1.127
Z-value...................: -1.222556
P-value...................: 0.2214974
Hosmer and Lemeshow r^2...: 0.050195
Cox and Snell r^2.........: 0.05154
Nagelkerke's pseudo r^2...: 0.079106
Sample size of AE DB......: 2388
Sample size of model......: 268
Missing data %............: 88.77722
- processing Fat10Perc
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Gender + Hypertension.composite +
DiabetesStatus + BMI, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) Gendermale Hypertension.compositeyes DiabetesStatusDiabetes BMI
-1.50676 0.92764 0.91937 -0.96551 0.06233
Degrees of Freedom: 268 Total (i.e. Null); 264 Residual
Null Deviance: 277.9
Residual Deviance: 258.8 AIC: 268.8
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + hsCRP_plasma, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.3536 0.3810 0.5203 0.6864 1.4348
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.139e+01 8.827e+02 0.013 0.98971
currentDF[, PROTEIN] 1.642e-01 1.690e-01 0.972 0.33115
Age -4.375e-04 2.173e-02 -0.020 0.98394
Gendermale 1.080e+00 3.462e-01 3.119 0.00181 **
Hypertension.compositeyes 8.208e-01 4.205e-01 1.952 0.05093 .
DiabetesStatusDiabetes -8.081e-01 4.286e-01 -1.886 0.05935 .
SmokerCurrentyes 3.842e-01 3.576e-01 1.074 0.28266
Med.Statin.LLDyes -3.148e-02 3.784e-01 -0.083 0.93371
Med.all.antiplateletyes 6.024e-01 6.006e-01 1.003 0.31587
GFR_MDRD -1.040e-02 1.041e-02 -0.999 0.31763
BMI 4.420e-02 4.424e-02 0.999 0.31774
CAD_history -4.480e-02 3.804e-01 -0.118 0.90624
Stroke_history -6.954e-03 3.719e-01 -0.019 0.98508
Peripheral.interv -1.508e-01 3.913e-01 -0.385 0.69991
stenose50-70% -1.453e+01 8.827e+02 -0.016 0.98686
stenose70-90% -1.212e+01 8.827e+02 -0.014 0.98905
stenose90-99% -1.233e+01 8.827e+02 -0.014 0.98886
stenose100% (Occlusion) -1.233e+01 8.827e+02 -0.014 0.98885
hsCRP_plasma -6.836e-04 1.841e-03 -0.371 0.71035
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 277.85 on 268 degrees of freedom
Residual deviance: 248.92 on 250 degrees of freedom
AIC: 286.92
Number of Fisher Scoring iterations: 13
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_rank ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_rank
Trait/outcome.............: Fat10Perc
Effect size...............: 0.164233
Standard error............: 0.168998
Odds ratio (effect size)..: 1.178
Lower 95% CI..............: 0.846
Upper 95% CI..............: 1.641
Z-value...................: 0.971804
P-value...................: 0.3311482
Hosmer and Lemeshow r^2...: 0.104119
Cox and Snell r^2.........: 0.101965
Nagelkerke's pseudo r^2...: 0.158323
Sample size of AE DB......: 2388
Sample size of model......: 269
Missing data %............: 88.73534
- processing IPH
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + Gender, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) Age Gendermale
-1.16709 0.02764 0.68346
Degrees of Freedom: 268 Total (i.e. Null); 266 Residual
Null Deviance: 299.8
Residual Deviance: 290.9 AIC: 296.9
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + hsCRP_plasma, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.17959 0.04718 0.63393 0.75588 1.32398
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 10.824949 882.746852 0.012 0.9902
currentDF[, PROTEIN] -0.061940 0.155846 -0.397 0.6910
Age 0.029119 0.019769 1.473 0.1408
Gendermale 0.759603 0.320114 2.373 0.0176 *
Hypertension.compositeyes 0.137913 0.416919 0.331 0.7408
DiabetesStatusDiabetes -0.485078 0.403890 -1.201 0.2297
SmokerCurrentyes 0.390151 0.322786 1.209 0.2268
Med.Statin.LLDyes -0.103067 0.340278 -0.303 0.7620
Med.all.antiplateletyes 0.293274 0.587081 0.500 0.6174
GFR_MDRD -0.001683 0.009405 -0.179 0.8580
BMI 0.023201 0.038976 0.595 0.5517
CAD_history 0.098145 0.346738 0.283 0.7771
Stroke_history -0.119419 0.339051 -0.352 0.7247
Peripheral.interv 0.185422 0.374163 0.496 0.6202
stenose50-70% -13.360543 882.744229 -0.015 0.9879
stenose70-90% -13.113999 882.743629 -0.015 0.9881
stenose90-99% -13.082823 882.743610 -0.015 0.9882
stenose100% (Occlusion) -12.806824 882.744499 -0.015 0.9884
hsCRP_plasma 0.003722 0.004579 0.813 0.4163
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 299.76 on 268 degrees of freedom
Residual deviance: 285.14 on 250 degrees of freedom
AIC: 323.14
Number of Fisher Scoring iterations: 13
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_rank ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_rank
Trait/outcome.............: IPH
Effect size...............: -0.06194
Standard error............: 0.155846
Odds ratio (effect size)..: 0.94
Lower 95% CI..............: 0.693
Upper 95% CI..............: 1.276
Z-value...................: -0.397442
P-value...................: 0.6910419
Hosmer and Lemeshow r^2...: 0.048752
Cox and Snell r^2.........: 0.052877
Nagelkerke's pseudo r^2...: 0.078701
Sample size of AE DB......: 2388
Sample size of model......: 269
Missing data %............: 88.73534
Analysis of MCP1_rank.
- processing CalcificationPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Hypertension.composite +
DiabetesStatus + Med.all.antiplatelet + Peripheral.interv,
family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) Hypertension.compositeyes DiabetesStatusDiabetes Med.all.antiplateletyes Peripheral.interv
-0.6342 0.5475 -0.6292 0.7942 -0.6265
Degrees of Freedom: 282 Total (i.e. Null); 278 Residual
Null Deviance: 385.1
Residual Deviance: 372.2 AIC: 382.2
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + hsCRP_plasma, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.8687 -1.1890 0.7609 0.9977 2.0563
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -1.603e+01 8.827e+02 -0.018 0.9855
currentDF[, PROTEIN] -1.311e-01 1.265e-01 -1.036 0.3002
Age 1.166e-02 1.704e-02 0.684 0.4941
Gendermale 1.499e-01 2.926e-01 0.512 0.6083
Hypertension.compositeyes 5.881e-01 3.663e-01 1.606 0.1084
DiabetesStatusDiabetes -7.689e-01 3.511e-01 -2.190 0.0285 *
SmokerCurrentyes 1.482e-01 2.779e-01 0.533 0.5939
Med.Statin.LLDyes -1.047e-01 2.928e-01 -0.358 0.7207
Med.all.antiplateletyes 1.198e+00 5.103e-01 2.348 0.0189 *
GFR_MDRD -3.765e-03 7.829e-03 -0.481 0.6306
BMI 6.503e-03 3.266e-02 0.199 0.8422
CAD_history 1.458e-01 2.957e-01 0.493 0.6219
Stroke_history -3.861e-01 2.831e-01 -1.364 0.1726
Peripheral.interv -6.597e-01 3.226e-01 -2.045 0.0409 *
stenose50-70% 1.484e+01 8.827e+02 0.017 0.9866
stenose70-90% 1.476e+01 8.827e+02 0.017 0.9867
stenose90-99% 1.408e+01 8.827e+02 0.016 0.9873
stenose100% (Occlusion) 1.553e+01 8.827e+02 0.018 0.9860
hsCRP_plasma 1.219e-04 1.575e-03 0.077 0.9383
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 385.14 on 282 degrees of freedom
Residual deviance: 360.92 on 264 degrees of freedom
AIC: 398.92
Number of Fisher Scoring iterations: 13
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: CalcificationPlaque
Effect size...............: -0.13109
Standard error............: 0.126542
Odds ratio (effect size)..: 0.877
Lower 95% CI..............: 0.684
Upper 95% CI..............: 1.124
Z-value...................: -1.035945
P-value...................: 0.3002277
Hosmer and Lemeshow r^2...: 0.062882
Cox and Snell r^2.........: 0.082016
Nagelkerke's pseudo r^2...: 0.110301
Sample size of AE DB......: 2388
Sample size of model......: 283
Missing data %............: 88.14908
- processing CollagenPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Stroke_history, family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Stroke_history
1.2479 -0.4765 0.4920
Degrees of Freedom: 281 Total (i.e. Null); 279 Residual
Null Deviance: 297.1
Residual Deviance: 283.9 AIC: 289.9
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + hsCRP_plasma, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.34755 0.06012 0.58686 0.73422 1.31594
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.748e+01 2.400e+03 0.007 0.99419
currentDF[, PROTEIN] -5.045e-01 1.592e-01 -3.170 0.00153 **
Age -5.547e-03 2.045e-02 -0.271 0.78618
Gendermale -7.921e-02 3.634e-01 -0.218 0.82743
Hypertension.compositeyes 2.925e-02 4.378e-01 0.067 0.94673
DiabetesStatusDiabetes 2.155e-01 4.403e-01 0.490 0.62446
SmokerCurrentyes 3.666e-01 3.426e-01 1.070 0.28459
Med.Statin.LLDyes 4.374e-02 3.430e-01 0.128 0.89854
Med.all.antiplateletyes 1.015e+00 5.424e-01 1.871 0.06138 .
GFR_MDRD -5.296e-03 9.597e-03 -0.552 0.58102
BMI -2.040e-02 4.045e-02 -0.504 0.61395
CAD_history 4.851e-02 3.488e-01 0.139 0.88938
Stroke_history 3.564e-01 3.577e-01 0.996 0.31907
Peripheral.interv -4.691e-01 3.713e-01 -1.264 0.20639
stenose50-70% -7.343e-01 2.623e+03 0.000 0.99978
stenose70-90% -1.569e+01 2.400e+03 -0.007 0.99478
stenose90-99% -1.608e+01 2.400e+03 -0.007 0.99465
stenose100% (Occlusion) -5.321e-01 2.709e+03 0.000 0.99984
hsCRP_plasma -6.307e-05 1.702e-03 -0.037 0.97043
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 297.07 on 281 degrees of freedom
Residual deviance: 272.39 on 263 degrees of freedom
AIC: 310.39
Number of Fisher Scoring iterations: 15
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: CollagenPlaque
Effect size...............: -0.50448
Standard error............: 0.159154
Odds ratio (effect size)..: 0.604
Lower 95% CI..............: 0.442
Upper 95% CI..............: 0.825
Z-value...................: -3.16975
P-value...................: 0.001525702
Hosmer and Lemeshow r^2...: 0.083107
Cox and Snell r^2.........: 0.083826
Nagelkerke's pseudo r^2...: 0.128712
Sample size of AE DB......: 2388
Sample size of model......: 282
Missing data %............: 88.19096
- processing Fat10Perc
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Gender + Hypertension.composite, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Gendermale Hypertension.compositeyes
0.0789 0.5522 0.5932 1.0357
Degrees of Freedom: 282 Total (i.e. Null); 279 Residual
Null Deviance: 295
Residual Deviance: 270.6 AIC: 278.6
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + hsCRP_plasma, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.3141 0.3124 0.5145 0.6850 1.5427
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.105e+01 8.827e+02 0.013 0.990009
currentDF[, PROTEIN] 5.645e-01 1.658e-01 3.405 0.000661 ***
Age 9.354e-03 2.085e-02 0.449 0.653654
Gendermale 6.345e-01 3.419e-01 1.856 0.063462 .
Hypertension.compositeyes 9.458e-01 4.203e-01 2.250 0.024443 *
DiabetesStatusDiabetes -2.360e-01 4.246e-01 -0.556 0.578317
SmokerCurrentyes 4.125e-01 3.493e-01 1.181 0.237653
Med.Statin.LLDyes 7.017e-03 3.696e-01 0.019 0.984853
Med.all.antiplateletyes 1.850e-01 5.906e-01 0.313 0.754077
GFR_MDRD -7.158e-03 9.898e-03 -0.723 0.469559
BMI 3.406e-02 4.044e-02 0.842 0.399695
CAD_history 4.642e-03 3.745e-01 0.012 0.990112
Stroke_history 6.665e-02 3.605e-01 0.185 0.853332
Peripheral.interv -3.889e-02 3.986e-01 -0.098 0.922261
stenose50-70% -1.430e+01 8.827e+02 -0.016 0.987078
stenose70-90% -1.185e+01 8.827e+02 -0.013 0.989291
stenose90-99% -1.235e+01 8.827e+02 -0.014 0.988834
stenose100% (Occlusion) -1.216e+01 8.827e+02 -0.014 0.989013
hsCRP_plasma -4.565e-04 1.916e-03 -0.238 0.811647
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 295.01 on 282 degrees of freedom
Residual deviance: 259.25 on 264 degrees of freedom
AIC: 297.25
Number of Fisher Scoring iterations: 13
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: Fat10Perc
Effect size...............: 0.564483
Standard error............: 0.165767
Odds ratio (effect size)..: 1.759
Lower 95% CI..............: 1.271
Upper 95% CI..............: 2.434
Z-value...................: 3.405272
P-value...................: 0.0006609828
Hosmer and Lemeshow r^2...: 0.121223
Cox and Snell r^2.........: 0.118709
Nagelkerke's pseudo r^2...: 0.183361
Sample size of AE DB......: 2388
Sample size of model......: 283
Missing data %............: 88.14908
- processing IPH
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Gender + DiabetesStatus,
family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) Gendermale DiabetesStatusDiabetes
0.6474 0.8393 -0.5088
Degrees of Freedom: 282 Total (i.e. Null); 280 Residual
Null Deviance: 316.6
Residual Deviance: 306.3 AIC: 312.3
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + hsCRP_plasma, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.21255 0.03224 0.60894 0.76603 1.53045
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.172e+01 8.827e+02 0.013 0.98941
currentDF[, PROTEIN] -1.546e-01 1.461e-01 -1.059 0.28982
Age 1.509e-02 1.947e-02 0.775 0.43807
Gendermale 9.703e-01 3.200e-01 3.032 0.00243 **
Hypertension.compositeyes 9.932e-02 4.094e-01 0.243 0.80834
DiabetesStatusDiabetes -5.111e-01 3.750e-01 -1.363 0.17298
SmokerCurrentyes 4.463e-01 3.232e-01 1.381 0.16740
Med.Statin.LLDyes -3.783e-01 3.469e-01 -1.091 0.27549
Med.all.antiplateletyes 4.714e-01 5.319e-01 0.886 0.37550
GFR_MDRD -8.638e-04 9.135e-03 -0.095 0.92467
BMI 1.923e-02 3.600e-02 0.534 0.59323
CAD_history 2.176e-01 3.461e-01 0.629 0.52963
Stroke_history -2.648e-02 3.306e-01 -0.080 0.93617
Peripheral.interv 2.434e-01 3.828e-01 0.636 0.52492
stenose50-70% -1.248e+01 8.827e+02 -0.014 0.98872
stenose70-90% -1.316e+01 8.827e+02 -0.015 0.98811
stenose90-99% -1.314e+01 8.827e+02 -0.015 0.98813
stenose100% (Occlusion) -1.352e+01 8.827e+02 -0.015 0.98778
hsCRP_plasma 3.179e-03 4.363e-03 0.729 0.46623
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 316.62 on 282 degrees of freedom
Residual deviance: 297.37 on 264 degrees of freedom
AIC: 335.37
Number of Fisher Scoring iterations: 13
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: IPH
Effect size...............: -0.154647
Standard error............: 0.146099
Odds ratio (effect size)..: 0.857
Lower 95% CI..............: 0.643
Upper 95% CI..............: 1.141
Z-value...................: -1.058506
P-value...................: 0.2898247
Hosmer and Lemeshow r^2...: 0.060794
Cox and Snell r^2.........: 0.065755
Nagelkerke's pseudo r^2...: 0.097656
Sample size of AE DB......: 2388
Sample size of model......: 283
Missing data %............: 88.14908
Analysis of IL6_pg_ug_2015_rank.
- processing CalcificationPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + SmokerCurrent +
CAD_history + Peripheral.interv + stenose, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) Age SmokerCurrentyes CAD_history Peripheral.interv
-2.03767 0.01906 0.31759 0.28534 -0.39470
stenose50-70% stenose70-90% stenose90-99% stenose100% (Occlusion) stenose70-99%
-0.26332 0.44676 0.69678 1.70987 -14.15643
Degrees of Freedom: 613 Total (i.e. Null); 604 Residual
Null Deviance: 849.9
Residual Deviance: 824.6 AIC: 844.6
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + hsCRP_plasma, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.639 -1.124 -0.782 1.160 1.713
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -3.125e+00 1.750e+00 -1.786 0.0741 .
currentDF[, PROTEIN] -2.045e-02 8.657e-02 -0.236 0.8133
Age 2.150e-02 1.055e-02 2.037 0.0416 *
Gendermale 3.066e-02 1.826e-01 0.168 0.8667
Hypertension.compositeyes 2.947e-01 2.552e-01 1.155 0.2482
DiabetesStatusDiabetes -2.433e-02 2.117e-01 -0.115 0.9085
SmokerCurrentyes 3.486e-01 1.844e-01 1.891 0.0586 .
Med.Statin.LLDyes -2.635e-01 2.026e-01 -1.301 0.1933
Med.all.antiplateletyes 6.608e-02 2.939e-01 0.225 0.8221
GFR_MDRD 4.748e-03 4.713e-03 1.007 0.3137
BMI 1.441e-02 2.247e-02 0.642 0.5212
CAD_history 2.976e-01 1.954e-01 1.523 0.1277
Stroke_history 4.809e-03 1.840e-01 0.026 0.9791
Peripheral.interv -3.832e-01 2.158e-01 -1.776 0.0758 .
stenose50-70% -1.930e-01 1.300e+00 -0.148 0.8820
stenose70-90% 5.134e-01 1.251e+00 0.410 0.6815
stenose90-99% 7.524e-01 1.251e+00 0.602 0.5474
stenose100% (Occlusion) 1.842e+00 1.718e+00 1.072 0.2838
stenose70-99% -1.407e+01 4.293e+02 -0.033 0.9739
hsCRP_plasma -3.991e-04 7.679e-04 -0.520 0.6032
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 849.91 on 613 degrees of freedom
Residual deviance: 819.87 on 594 degrees of freedom
AIC: 859.87
Number of Fisher Scoring iterations: 13
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_rank ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_pg_ug_2015_rank
Trait/outcome.............: CalcificationPlaque
Effect size...............: -0.020447
Standard error............: 0.086566
Odds ratio (effect size)..: 0.98
Lower 95% CI..............: 0.827
Upper 95% CI..............: 1.161
Z-value...................: -0.236202
P-value...................: 0.8132758
Hosmer and Lemeshow r^2...: 0.035343
Cox and Snell r^2.........: 0.047745
Nagelkerke's pseudo r^2...: 0.063704
Sample size of AE DB......: 2388
Sample size of model......: 614
Missing data %............: 74.28811
- processing CollagenPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
SmokerCurrent + CAD_history, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] SmokerCurrentyes CAD_history
0.9687 -0.3028 0.4678 0.5081
Degrees of Freedom: 616 Total (i.e. Null); 613 Residual
Null Deviance: 658.4
Residual Deviance: 639.9 AIC: 647.9
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + hsCRP_plasma, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.3183 0.3944 0.6278 0.7480 1.1415
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.308e+01 8.347e+02 0.016 0.98750
currentDF[, PROTEIN] -3.246e-01 1.069e-01 -3.036 0.00239 **
Age 6.979e-03 1.231e-02 0.567 0.57092
Gendermale 7.282e-02 2.168e-01 0.336 0.73693
Hypertension.compositeyes 1.616e-01 2.881e-01 0.561 0.57480
DiabetesStatusDiabetes 2.080e-01 2.608e-01 0.797 0.42523
SmokerCurrentyes 5.486e-01 2.293e-01 2.393 0.01671 *
Med.Statin.LLDyes -1.187e-01 2.418e-01 -0.491 0.62337
Med.all.antiplateletyes 5.316e-01 3.297e-01 1.612 0.10691
GFR_MDRD 2.491e-03 5.598e-03 0.445 0.65630
BMI 2.510e-02 2.805e-02 0.895 0.37089
CAD_history 5.359e-01 2.479e-01 2.162 0.03064 *
Stroke_history 3.038e-01 2.237e-01 1.358 0.17445
Peripheral.interv 9.879e-02 2.631e-01 0.376 0.70726
stenose50-70% -1.414e+01 8.347e+02 -0.017 0.98648
stenose70-90% -1.421e+01 8.347e+02 -0.017 0.98642
stenose90-99% -1.415e+01 8.347e+02 -0.017 0.98647
stenose100% (Occlusion) 6.239e-01 1.074e+03 0.001 0.99954
stenose70-99% -1.443e+01 8.347e+02 -0.017 0.98620
hsCRP_plasma -7.901e-04 7.316e-04 -1.080 0.28013
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 658.36 on 616 degrees of freedom
Residual deviance: 627.90 on 597 degrees of freedom
AIC: 667.9
Number of Fisher Scoring iterations: 14
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_rank ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_pg_ug_2015_rank
Trait/outcome.............: CollagenPlaque
Effect size...............: -0.324626
Standard error............: 0.106915
Odds ratio (effect size)..: 0.723
Lower 95% CI..............: 0.586
Upper 95% CI..............: 0.891
Z-value...................: -3.036315
P-value...................: 0.002394889
Hosmer and Lemeshow r^2...: 0.046255
Cox and Snell r^2.........: 0.048157
Nagelkerke's pseudo r^2...: 0.073414
Sample size of AE DB......: 2388
Sample size of model......: 617
Missing data %............: 74.16248
- processing Fat10Perc
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Gender + Peripheral.interv, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Gendermale Peripheral.interv
0.5293 0.6114 0.8125 -0.4397
Degrees of Freedom: 616 Total (i.e. Null); 613 Residual
Null Deviance: 743.2
Residual Deviance: 680.1 AIC: 688.1
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + hsCRP_plasma, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.2677 -1.0403 0.6028 0.8173 2.0610
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.352e+01 5.059e+02 0.027 0.9787
currentDF[, PROTEIN] 6.055e-01 1.037e-01 5.838 5.28e-09 ***
Age 6.235e-03 1.200e-02 0.520 0.6033
Gendermale 8.432e-01 2.013e-01 4.189 2.80e-05 ***
Hypertension.compositeyes 1.228e-01 2.865e-01 0.429 0.6682
DiabetesStatusDiabetes -3.444e-02 2.411e-01 -0.143 0.8864
SmokerCurrentyes -5.286e-03 2.111e-01 -0.025 0.9800
Med.Statin.LLDyes -5.567e-02 2.356e-01 -0.236 0.8132
Med.all.antiplateletyes -5.235e-03 3.311e-01 -0.016 0.9874
GFR_MDRD -1.149e-03 5.415e-03 -0.212 0.8320
BMI 8.579e-03 2.474e-02 0.347 0.7288
CAD_history -6.148e-02 2.220e-01 -0.277 0.7818
Stroke_history 2.278e-01 2.165e-01 1.052 0.2928
Peripheral.interv -4.543e-01 2.339e-01 -1.942 0.0521 .
stenose50-70% -1.407e+01 5.059e+02 -0.028 0.9778
stenose70-90% -1.371e+01 5.059e+02 -0.027 0.9784
stenose90-99% -1.360e+01 5.059e+02 -0.027 0.9786
stenose100% (Occlusion) -1.511e+01 5.059e+02 -0.030 0.9762
stenose70-99% -1.475e+01 5.059e+02 -0.029 0.9767
hsCRP_plasma 8.231e-04 1.316e-03 0.625 0.5318
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 743.18 on 616 degrees of freedom
Residual deviance: 671.07 on 597 degrees of freedom
AIC: 711.07
Number of Fisher Scoring iterations: 13
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_rank ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_pg_ug_2015_rank
Trait/outcome.............: Fat10Perc
Effect size...............: 0.605506
Standard error............: 0.103716
Odds ratio (effect size)..: 1.832
Lower 95% CI..............: 1.495
Upper 95% CI..............: 2.245
Z-value...................: 5.838117
P-value...................: 5.279419e-09
Hosmer and Lemeshow r^2...: 0.097027
Cox and Snell r^2.........: 0.110299
Nagelkerke's pseudo r^2...: 0.157534
Sample size of AE DB......: 2388
Sample size of model......: 617
Missing data %............: 74.16248
- processing IPH
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Gender + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) Gendermale SmokerCurrentyes Med.Statin.LLDyes Med.all.antiplateletyes
-0.2863 0.6645 0.4139 -0.3598 0.4213
Degrees of Freedom: 615 Total (i.e. Null); 611 Residual
Null Deviance: 828.8
Residual Deviance: 806.9 AIC: 816.9
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + hsCRP_plasma, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.0878 -1.2415 0.8000 0.9943 1.5548
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.3978635 1.7606654 0.226 0.821222
currentDF[, PROTEIN] 0.1303245 0.0892699 1.460 0.144320
Age -0.0008199 0.0107395 -0.076 0.939143
Gendermale 0.7038625 0.1860745 3.783 0.000155 ***
Hypertension.compositeyes -0.3438930 0.2655006 -1.295 0.195230
DiabetesStatusDiabetes -0.1476838 0.2140216 -0.690 0.490168
SmokerCurrentyes 0.3955331 0.1923278 2.057 0.039729 *
Med.Statin.LLDyes -0.3590541 0.2126520 -1.688 0.091323 .
Med.all.antiplateletyes 0.3879883 0.2955953 1.313 0.189329
GFR_MDRD -0.0050049 0.0048318 -1.036 0.300281
BMI 0.0091710 0.0228344 0.402 0.687956
CAD_history 0.2170901 0.2010528 1.080 0.280246
Stroke_history 0.2138322 0.1908381 1.120 0.262505
Peripheral.interv 0.1016178 0.2212728 0.459 0.646060
stenose50-70% -0.7340905 1.2832124 -0.572 0.567273
stenose70-90% -0.4135027 1.2420292 -0.333 0.739191
stenose90-99% -0.1350682 1.2420013 -0.109 0.913400
stenose100% (Occlusion) -0.8891041 1.6032226 -0.555 0.579187
stenose70-99% 0.3924623 1.7022428 0.231 0.817660
hsCRP_plasma -0.0006085 0.0007988 -0.762 0.446223
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 828.82 on 615 degrees of freedom
Residual deviance: 794.02 on 596 degrees of freedom
AIC: 834.02
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_rank ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_pg_ug_2015_rank
Trait/outcome.............: IPH
Effect size...............: 0.130324
Standard error............: 0.08927
Odds ratio (effect size)..: 1.139
Lower 95% CI..............: 0.956
Upper 95% CI..............: 1.357
Z-value...................: 1.459892
P-value...................: 0.1443198
Hosmer and Lemeshow r^2...: 0.041991
Cox and Snell r^2.........: 0.054933
Nagelkerke's pseudo r^2...: 0.074274
Sample size of AE DB......: 2388
Sample size of model......: 616
Missing data %............: 74.20436
Analysis of IL6R_pg_ug_2015_rank.
- processing CalcificationPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + SmokerCurrent +
CAD_history + Peripheral.interv + stenose, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) Age SmokerCurrentyes CAD_history Peripheral.interv
-1.39761 0.01805 0.26766 0.28298 -0.44359
stenose50-70% stenose70-90% stenose90-99% stenose100% (Occlusion) stenose50-99%
-0.76341 -0.08234 0.14517 1.15737 -15.67282
stenose70-99%
-15.68045
Degrees of Freedom: 618 Total (i.e. Null); 608 Residual
Null Deviance: 856.9
Residual Deviance: 830.8 AIC: 852.8
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + hsCRP_plasma, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.5362 -1.1336 -0.7777 1.1683 1.7316
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -2.244e+00 1.864e+00 -1.203 0.2288
currentDF[, PROTEIN] -2.870e-02 8.420e-02 -0.341 0.7332
Age 2.035e-02 1.052e-02 1.933 0.0532 .
Gendermale -7.018e-03 1.812e-01 -0.039 0.9691
Hypertension.compositeyes 2.763e-01 2.520e-01 1.097 0.2729
DiabetesStatusDiabetes -1.002e-01 2.111e-01 -0.474 0.6352
SmokerCurrentyes 2.927e-01 1.830e-01 1.600 0.1097
Med.Statin.LLDyes -1.664e-01 2.054e-01 -0.810 0.4177
Med.all.antiplateletyes 1.380e-01 2.936e-01 0.470 0.6384
GFR_MDRD 3.991e-03 4.690e-03 0.851 0.3947
BMI 3.507e-03 2.295e-02 0.153 0.8786
CAD_history 2.929e-01 1.945e-01 1.506 0.1320
Stroke_history -1.298e-02 1.821e-01 -0.071 0.9432
Peripheral.interv -4.343e-01 2.163e-01 -2.008 0.0447 *
stenose50-70% -6.697e-01 1.476e+00 -0.454 0.6501
stenose70-90% 2.169e-02 1.434e+00 0.015 0.9879
stenose90-99% 2.411e-01 1.436e+00 0.168 0.8666
stenose100% (Occlusion) 1.304e+00 1.854e+00 0.703 0.4817
stenose50-99% -1.567e+01 1.029e+03 -0.015 0.9879
stenose70-99% -1.564e+01 7.110e+02 -0.022 0.9824
hsCRP_plasma -4.118e-04 7.572e-04 -0.544 0.5865
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 856.94 on 618 degrees of freedom
Residual deviance: 827.42 on 598 degrees of freedom
AIC: 869.42
Number of Fisher Scoring iterations: 14
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_rank ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6R_pg_ug_2015_rank
Trait/outcome.............: CalcificationPlaque
Effect size...............: -0.028703
Standard error............: 0.0842
Odds ratio (effect size)..: 0.972
Lower 95% CI..............: 0.824
Upper 95% CI..............: 1.146
Z-value...................: -0.340892
P-value...................: 0.7331848
Hosmer and Lemeshow r^2...: 0.034446
Cox and Snell r^2.........: 0.046567
Nagelkerke's pseudo r^2...: 0.062129
Sample size of AE DB......: 2388
Sample size of model......: 619
Missing data %............: 74.07873
- processing CollagenPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ SmokerCurrent +
Med.all.antiplatelet + CAD_history, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) SmokerCurrentyes Med.all.antiplateletyes CAD_history
0.4945 0.4512 0.4820 0.5581
Degrees of Freedom: 621 Total (i.e. Null); 618 Residual
Null Deviance: 665.8
Residual Deviance: 653.9 AIC: 661.9
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + hsCRP_plasma, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.2594 0.4425 0.6377 0.7661 1.1346
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.310e+01 1.011e+03 0.013 0.9897
currentDF[, PROTEIN] 7.121e-03 1.002e-01 0.071 0.9433
Age 9.646e-03 1.226e-02 0.787 0.4312
Gendermale 1.459e-01 2.117e-01 0.689 0.4909
Hypertension.compositeyes 1.036e-01 2.844e-01 0.364 0.7157
DiabetesStatusDiabetes 2.299e-01 2.587e-01 0.889 0.3742
SmokerCurrentyes 5.375e-01 2.260e-01 2.379 0.0174 *
Med.Statin.LLDyes -2.465e-02 2.401e-01 -0.103 0.9182
Med.all.antiplateletyes 5.599e-01 3.220e-01 1.739 0.0820 .
GFR_MDRD 1.810e-03 5.550e-03 0.326 0.7443
BMI 2.302e-02 2.772e-02 0.830 0.4064
CAD_history 5.432e-01 2.439e-01 2.227 0.0260 *
Stroke_history 1.954e-01 2.189e-01 0.893 0.3720
Peripheral.interv 1.382e-01 2.615e-01 0.529 0.5971
stenose50-70% -1.432e+01 1.011e+03 -0.014 0.9887
stenose70-90% -1.446e+01 1.011e+03 -0.014 0.9886
stenose90-99% -1.431e+01 1.011e+03 -0.014 0.9887
stenose100% (Occlusion) 3.060e-01 1.229e+03 0.000 0.9998
stenose50-99% -4.011e-01 1.442e+03 0.000 0.9998
stenose70-99% -1.468e+01 1.011e+03 -0.015 0.9884
hsCRP_plasma -8.393e-04 7.374e-04 -1.138 0.2551
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 665.84 on 621 degrees of freedom
Residual deviance: 643.58 on 601 degrees of freedom
AIC: 685.58
Number of Fisher Scoring iterations: 14
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_rank ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6R_pg_ug_2015_rank
Trait/outcome.............: CollagenPlaque
Effect size...............: 0.007121
Standard error............: 0.10019
Odds ratio (effect size)..: 1.007
Lower 95% CI..............: 0.828
Upper 95% CI..............: 1.226
Z-value...................: 0.071079
P-value...................: 0.943335
Hosmer and Lemeshow r^2...: 0.033434
Cox and Snell r^2.........: 0.035158
Nagelkerke's pseudo r^2...: 0.0535
Sample size of AE DB......: 2388
Sample size of model......: 622
Missing data %............: 73.9531
- processing Fat10Perc
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Gender + Stroke_history + Peripheral.interv, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Gendermale Stroke_history Peripheral.interv
0.3305 0.2398 0.8514 0.3619 -0.4835
Degrees of Freedom: 621 Total (i.e. Null); 617 Residual
Null Deviance: 750.2
Residual Deviance: 714.3 AIC: 724.3
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + hsCRP_plasma, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.1084 -1.1775 0.6665 0.8395 1.7256
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.329e+01 6.236e+02 0.021 0.9830
currentDF[, PROTEIN] 2.360e-01 9.449e-02 2.497 0.0125 *
Age 9.854e-03 1.165e-02 0.846 0.3975
Gendermale 8.899e-01 1.953e-01 4.557 5.2e-06 ***
Hypertension.compositeyes 1.338e-01 2.763e-01 0.484 0.6281
DiabetesStatusDiabetes -1.547e-02 2.337e-01 -0.066 0.9472
SmokerCurrentyes 5.933e-02 2.054e-01 0.289 0.7727
Med.Statin.LLDyes 6.082e-02 2.329e-01 0.261 0.7940
Med.all.antiplateletyes 5.539e-02 3.245e-01 0.171 0.8645
GFR_MDRD 1.544e-04 5.301e-03 0.029 0.9768
BMI -4.085e-03 2.512e-02 -0.163 0.8708
CAD_history -1.715e-01 2.159e-01 -0.794 0.4271
Stroke_history 3.399e-01 2.103e-01 1.616 0.1061
Peripheral.interv -4.900e-01 2.288e-01 -2.142 0.0322 *
stenose50-70% -1.406e+01 6.236e+02 -0.023 0.9820
stenose70-90% -1.370e+01 6.236e+02 -0.022 0.9825
stenose90-99% -1.372e+01 6.236e+02 -0.022 0.9824
stenose100% (Occlusion) -1.499e+01 6.236e+02 -0.024 0.9808
stenose50-99% -2.925e+01 8.788e+02 -0.033 0.9734
stenose70-99% -1.440e+01 6.236e+02 -0.023 0.9816
hsCRP_plasma 1.055e-03 1.180e-03 0.894 0.3712
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 750.18 on 621 degrees of freedom
Residual deviance: 702.66 on 601 degrees of freedom
AIC: 744.66
Number of Fisher Scoring iterations: 13
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_rank ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6R_pg_ug_2015_rank
Trait/outcome.............: Fat10Perc
Effect size...............: 0.235974
Standard error............: 0.09449
Odds ratio (effect size)..: 1.266
Lower 95% CI..............: 1.052
Upper 95% CI..............: 1.524
Z-value...................: 2.497357
P-value...................: 0.0125123
Hosmer and Lemeshow r^2...: 0.06335
Cox and Snell r^2.........: 0.07356
Nagelkerke's pseudo r^2...: 0.10499
Sample size of AE DB......: 2388
Sample size of model......: 622
Missing data %............: 73.9531
- processing IPH
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Gender + SmokerCurrent, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Gendermale SmokerCurrentyes
-0.1522 0.2498 0.6365 0.3340
Degrees of Freedom: 619 Total (i.e. Null); 616 Residual
Null Deviance: 835.3
Residual Deviance: 810.9 AIC: 818.9
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + hsCRP_plasma, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.0278 -1.2499 0.7814 0.9985 1.6067
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 4.328e-01 1.886e+00 0.229 0.818487
currentDF[, PROTEIN] 2.194e-01 8.702e-02 2.521 0.011704 *
Age -2.754e-03 1.079e-02 -0.255 0.798489
Gendermale 6.859e-01 1.848e-01 3.711 0.000206 ***
Hypertension.compositeyes -2.878e-01 2.614e-01 -1.101 0.270823
DiabetesStatusDiabetes -1.545e-01 2.130e-01 -0.725 0.468254
SmokerCurrentyes 3.034e-01 1.908e-01 1.590 0.111766
Med.Statin.LLDyes -2.655e-01 2.148e-01 -1.236 0.216525
Med.all.antiplateletyes 3.741e-01 2.947e-01 1.270 0.204246
GFR_MDRD -4.773e-03 4.840e-03 -0.986 0.324056
BMI -6.643e-03 2.344e-02 -0.283 0.776888
CAD_history 1.800e-01 2.001e-01 0.899 0.368391
Stroke_history 1.644e-01 1.892e-01 0.869 0.384946
Peripheral.interv 4.845e-02 2.214e-01 0.219 0.826790
stenose50-70% -1.988e-01 1.475e+00 -0.135 0.892768
stenose70-90% 8.911e-02 1.438e+00 0.062 0.950580
stenose90-99% 2.268e-01 1.440e+00 0.158 0.874809
stenose100% (Occlusion) -3.762e-01 1.763e+00 -0.213 0.831001
stenose50-99% 1.457e+01 6.226e+02 0.023 0.981333
stenose70-99% 1.153e+00 1.844e+00 0.625 0.531866
hsCRP_plasma -4.849e-04 7.984e-04 -0.607 0.543636
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 835.34 on 619 degrees of freedom
Residual deviance: 798.71 on 599 degrees of freedom
AIC: 840.71
Number of Fisher Scoring iterations: 13
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_rank ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6R_pg_ug_2015_rank
Trait/outcome.............: IPH
Effect size...............: 0.21938
Standard error............: 0.087023
Odds ratio (effect size)..: 1.245
Lower 95% CI..............: 1.05
Upper 95% CI..............: 1.477
Z-value...................: 2.520944
P-value...................: 0.01170406
Hosmer and Lemeshow r^2...: 0.04385
Cox and Snell r^2.........: 0.057369
Nagelkerke's pseudo r^2...: 0.077519
Sample size of AE DB......: 2388
Sample size of model......: 620
Missing data %............: 74.03685
Analysis of MCP1_pg_ug_2015_rank.
- processing CalcificationPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + SmokerCurrent + Peripheral.interv + stenose, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Age SmokerCurrentyes Peripheral.interv
-2.39988 -0.44033 0.02181 0.26603 -0.41214
stenose50-70% stenose70-90% stenose90-99% stenose100% (Occlusion) stenose50-99%
0.10233 0.80161 0.93392 1.61242 -14.94601
stenose70-99%
-14.58139
Degrees of Freedom: 637 Total (i.e. Null); 627 Residual
Null Deviance: 882.6
Residual Deviance: 828.8 AIC: 850.8
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + hsCRP_plasma, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.7296 -1.0748 -0.6531 1.1213 1.9692
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -3.185e+00 1.759e+00 -1.811 0.0701 .
currentDF[, PROTEIN] -4.475e-01 8.767e-02 -5.104 3.32e-07 ***
Age 2.189e-02 1.062e-02 2.061 0.0393 *
Gendermale 4.745e-02 1.830e-01 0.259 0.7954
Hypertension.compositeyes 2.459e-01 2.551e-01 0.964 0.3351
DiabetesStatusDiabetes -1.058e-01 2.126e-01 -0.498 0.6186
SmokerCurrentyes 2.913e-01 1.842e-01 1.582 0.1137
Med.Statin.LLDyes -2.958e-01 2.055e-01 -1.440 0.1500
Med.all.antiplateletyes 1.271e-01 2.933e-01 0.434 0.6646
GFR_MDRD 4.087e-03 4.747e-03 0.861 0.3893
BMI 9.852e-03 2.247e-02 0.438 0.6611
CAD_history 2.717e-01 1.945e-01 1.397 0.1625
Stroke_history 1.043e-01 1.845e-01 0.565 0.5719
Peripheral.interv -4.251e-01 2.167e-01 -1.962 0.0498 *
stenose50-70% 8.138e-02 1.294e+00 0.063 0.9498
stenose70-90% 8.083e-01 1.243e+00 0.650 0.5156
stenose90-99% 9.315e-01 1.243e+00 0.749 0.4536
stenose100% (Occlusion) 1.628e+00 1.713e+00 0.950 0.3420
stenose50-99% -1.493e+01 9.904e+02 -0.015 0.9880
stenose70-99% -1.457e+01 6.868e+02 -0.021 0.9831
hsCRP_plasma -1.953e-04 8.261e-04 -0.236 0.8131
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 882.64 on 637 degrees of freedom
Residual deviance: 822.45 on 617 degrees of freedom
AIC: 864.45
Number of Fisher Scoring iterations: 14
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: CalcificationPlaque
Effect size...............: -0.447486
Standard error............: 0.087672
Odds ratio (effect size)..: 0.639
Lower 95% CI..............: 0.538
Upper 95% CI..............: 0.759
Z-value...................: -5.104117
P-value...................: 3.323428e-07
Hosmer and Lemeshow r^2...: 0.068196
Cox and Snell r^2.........: 0.090032
Nagelkerke's pseudo r^2...: 0.120157
Sample size of AE DB......: 2388
Sample size of model......: 638
Missing data %............: 73.28308
- processing CollagenPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
SmokerCurrent + Med.all.antiplatelet + CAD_history, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] SmokerCurrentyes Med.all.antiplateletyes CAD_history
0.5805 -0.2561 0.4792 0.4410 0.5331
Degrees of Freedom: 640 Total (i.e. Null); 636 Residual
Null Deviance: 683
Residual Deviance: 663.3 AIC: 673.3
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + hsCRP_plasma, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.3079 0.3988 0.6302 0.7500 1.1886
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.304e+01 8.257e+02 0.016 0.9874
currentDF[, PROTEIN] -2.416e-01 9.930e-02 -2.433 0.0150 *
Age 7.120e-03 1.214e-02 0.586 0.5577
Gendermale 1.621e-01 2.111e-01 0.768 0.4425
Hypertension.compositeyes 6.908e-02 2.849e-01 0.243 0.8084
DiabetesStatusDiabetes 1.106e-01 2.528e-01 0.438 0.6616
SmokerCurrentyes 5.456e-01 2.252e-01 2.423 0.0154 *
Med.Statin.LLDyes -7.351e-02 2.379e-01 -0.309 0.7574
Med.all.antiplateletyes 4.861e-01 3.204e-01 1.517 0.1293
GFR_MDRD 2.182e-03 5.484e-03 0.398 0.6908
BMI 3.061e-02 2.736e-02 1.119 0.2632
CAD_history 5.312e-01 2.407e-01 2.207 0.0273 *
Stroke_history 2.544e-01 2.187e-01 1.163 0.2447
Peripheral.interv 1.899e-01 2.604e-01 0.729 0.4658
stenose50-70% -1.411e+01 8.257e+02 -0.017 0.9864
stenose70-90% -1.426e+01 8.257e+02 -0.017 0.9862
stenose90-99% -1.418e+01 8.257e+02 -0.017 0.9863
stenose100% (Occlusion) 1.502e-01 1.086e+03 0.000 0.9999
stenose50-99% -2.239e-01 1.316e+03 0.000 0.9999
stenose70-99% -1.432e+01 8.257e+02 -0.017 0.9862
hsCRP_plasma -7.554e-04 7.479e-04 -1.010 0.3125
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 682.96 on 640 degrees of freedom
Residual deviance: 653.34 on 620 degrees of freedom
AIC: 695.34
Number of Fisher Scoring iterations: 14
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: CollagenPlaque
Effect size...............: -0.241606
Standard error............: 0.099301
Odds ratio (effect size)..: 0.785
Lower 95% CI..............: 0.646
Upper 95% CI..............: 0.954
Z-value...................: -2.433053
P-value...................: 0.01497212
Hosmer and Lemeshow r^2...: 0.043362
Cox and Snell r^2.........: 0.04515
Nagelkerke's pseudo r^2...: 0.068885
Sample size of AE DB......: 2388
Sample size of model......: 641
Missing data %............: 73.15745
- processing Fat10Perc
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Gender + Stroke_history + Peripheral.interv, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Gendermale Stroke_history Peripheral.interv
0.2756 0.2211 0.8570 0.3349 -0.3642
Degrees of Freedom: 640 Total (i.e. Null); 636 Residual
Null Deviance: 779.2
Residual Deviance: 743 AIC: 753
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + hsCRP_plasma, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.0590 -1.1830 0.6686 0.8340 1.5657
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.421e+01 8.368e+02 0.017 0.9865
currentDF[, PROTEIN] 2.300e-01 9.342e-02 2.462 0.0138 *
Age 6.612e-03 1.139e-02 0.581 0.5615
Gendermale 8.980e-01 1.913e-01 4.695 2.67e-06 ***
Hypertension.compositeyes 1.628e-01 2.722e-01 0.598 0.5498
DiabetesStatusDiabetes -5.155e-02 2.282e-01 -0.226 0.8213
SmokerCurrentyes 7.727e-02 2.002e-01 0.386 0.6996
Med.Statin.LLDyes -7.564e-02 2.280e-01 -0.332 0.7401
Med.all.antiplateletyes 8.708e-02 3.168e-01 0.275 0.7834
GFR_MDRD -1.562e-04 5.212e-03 -0.030 0.9761
BMI 4.533e-03 2.369e-02 0.191 0.8483
CAD_history -1.616e-01 2.093e-01 -0.772 0.4402
Stroke_history 3.225e-01 2.074e-01 1.555 0.1199
Peripheral.interv -3.953e-01 2.238e-01 -1.766 0.0773 .
stenose50-70% -1.508e+01 8.368e+02 -0.018 0.9856
stenose70-90% -1.464e+01 8.368e+02 -0.017 0.9860
stenose90-99% -1.456e+01 8.368e+02 -0.017 0.9861
stenose100% (Occlusion) -1.569e+01 8.368e+02 -0.019 0.9850
stenose50-99% -3.120e+01 1.302e+03 -0.024 0.9809
stenose70-99% -1.570e+01 8.368e+02 -0.019 0.9850
hsCRP_plasma 9.345e-04 1.300e-03 0.719 0.4721
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 779.19 on 640 degrees of freedom
Residual deviance: 729.62 on 620 degrees of freedom
AIC: 771.62
Number of Fisher Scoring iterations: 14
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: Fat10Perc
Effect size...............: 0.229996
Standard error............: 0.093417
Odds ratio (effect size)..: 1.259
Lower 95% CI..............: 1.048
Upper 95% CI..............: 1.511
Z-value...................: 2.462037
P-value...................: 0.01381502
Hosmer and Lemeshow r^2...: 0.063619
Cox and Snell r^2.........: 0.07442
Nagelkerke's pseudo r^2...: 0.105791
Sample size of AE DB......: 2388
Sample size of model......: 641
Missing data %............: 73.15745
- processing IPH
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Gender + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) Gendermale SmokerCurrentyes Med.Statin.LLDyes Med.all.antiplateletyes
-0.2358 0.6317 0.4079 -0.4060 0.4192
Degrees of Freedom: 638 Total (i.e. Null); 634 Residual
Null Deviance: 861.2
Residual Deviance: 838.9 AIC: 848.9
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + hsCRP_plasma, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.9749 -1.2424 0.7969 1.0058 1.5314
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 8.622e-01 1.750e+00 0.493 0.622313
currentDF[, PROTEIN] -6.833e-02 8.480e-02 -0.806 0.420364
Age -4.438e-03 1.057e-02 -0.420 0.674652
Gendermale 6.920e-01 1.823e-01 3.796 0.000147 ***
Hypertension.compositeyes -3.447e-01 2.594e-01 -1.329 0.183993
DiabetesStatusDiabetes -1.860e-01 2.087e-01 -0.891 0.372807
SmokerCurrentyes 3.582e-01 1.871e-01 1.914 0.055564 .
Med.Statin.LLDyes -4.437e-01 2.115e-01 -2.097 0.035959 *
Med.all.antiplateletyes 3.849e-01 2.892e-01 1.331 0.183178
GFR_MDRD -5.926e-03 4.758e-03 -1.246 0.212891
BMI 3.031e-03 2.235e-02 0.136 0.892158
CAD_history 1.916e-01 1.949e-01 0.983 0.325626
Stroke_history 2.615e-01 1.877e-01 1.393 0.163546
Peripheral.interv 1.459e-01 2.169e-01 0.673 0.500968
stenose50-70% -6.404e-01 1.287e+00 -0.498 0.618782
stenose70-90% -2.939e-01 1.246e+00 -0.236 0.813503
stenose90-99% -1.048e-01 1.245e+00 -0.084 0.932958
stenose100% (Occlusion) -8.639e-01 1.609e+00 -0.537 0.591219
stenose50-99% 1.414e+01 6.240e+02 0.023 0.981926
stenose70-99% 5.429e-01 1.702e+00 0.319 0.749796
hsCRP_plasma -5.275e-04 7.988e-04 -0.660 0.509031
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 861.23 on 638 degrees of freedom
Residual deviance: 825.59 on 618 degrees of freedom
AIC: 867.59
Number of Fisher Scoring iterations: 13
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: IPH
Effect size...............: -0.068328
Standard error............: 0.084797
Odds ratio (effect size)..: 0.934
Lower 95% CI..............: 0.791
Upper 95% CI..............: 1.103
Z-value...................: -0.80579
P-value...................: 0.4203639
Hosmer and Lemeshow r^2...: 0.041381
Cox and Snell r^2.........: 0.054245
Nagelkerke's pseudo r^2...: 0.073286
Sample size of AE DB......: 2388
Sample size of model......: 639
Missing data %............: 73.24121 cat("Edit the column names...\n")Edit the column names...colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
"Beta", "s.e.m.",
"OR", "low95CI", "up95CI",
"Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")
cat("Correct the variable types...\n")Correct the variable types...GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)
# Save the data
cat("Writing results to Excel-file...\n")Writing results to Excel-file...### Univariate
write.xlsx(GLM.results,
file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Multi.Protein.PlaquePhenotypes.RANK.MODEL4.xlsx"),
row.names = FALSE, col.names = TRUE, sheetName = "Bin.Multi.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")Removing intermediate files...rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)
Model 5
In this model we correct for Age, Gender, Hypertension status, Diabetes status, current smoker status, lipid-lowering drugs (LLDs), antiplatelet medication, eGFR (MDRD), BMI, CAD history, stroke history, peripheral interventions, stenosis, and IL6 in plaques.
Natural log-transformed data
First we use the natural-log transformed data.
Analysis of continuous/quantitative plaque traits as a function of serum/plaque IL6(R)/MCP1 levels.
GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")Running linear regression...for (protein in 1:length(TRAITS.PROTEIN)) {
PROTEIN = TRAITS.PROTEIN[protein]
cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
for (trait in 1:length(TRAITS.CON)) {
TRAIT = TRAITS.CON[trait]
cat(paste0("\n- processing ",TRAIT,"\n\n"))
currentDF <- as.data.frame(AEDB.CEA %>%
dplyr::select(., PROTEIN, TRAIT, COVARIATES_M5) %>%
filter(complete.cases(.))) %>%
filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
# for debug
# print(DT::datatable(currentDF))
# print(nrow(currentDF))
# print(str(currentDF))
### univariate
fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender +
Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose + IL6_pg_ug_2015_LN,
data = currentDF)
model_step <- stepAIC(fit, direction = "both", trace = FALSE)
print(model_step)
print(summary(fit))
GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
GLM.results.TEMP[1,] = GLM.CON(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
GLM.results = rbind(GLM.results, GLM.results.TEMP)
}
}
Analysis of IL6_LN.
- processing Macrophages_LNNote: Using an external vector in selections is ambiguous.
[34mℹ[39m Use `all_of(COVARIATES_M5)` instead of `COVARIATES_M5` to silence this message.
[34mℹ[39m See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
[90mThis message is displayed once per session.[39m
Call:
lm(formula = currentDF[, PROTEIN] ~ IL6_pg_ug_2015_LN, data = currentDF)
Coefficients:
(Intercept) IL6_pg_ug_2015_LN
4.611 0.185
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose +
IL6_pg_ug_2015_LN, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.4165 -0.8007 0.0170 0.6097 3.9655
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.927383 1.271370 4.662 4.78e-06 ***
currentDF[, TRAIT] -0.021467 0.030894 -0.695 0.488
Age -0.001101 0.008324 -0.132 0.895
Gendermale -0.138367 0.141507 -0.978 0.329
Hypertension.compositeyes -0.050626 0.198464 -0.255 0.799
DiabetesStatusDiabetes 0.083022 0.160017 0.519 0.604
SmokerCurrentyes -0.060466 0.140820 -0.429 0.668
Med.Statin.LLDyes -0.227598 0.142187 -1.601 0.111
Med.all.antiplateletyes -0.169649 0.215412 -0.788 0.432
GFR_MDRD 0.004727 0.003655 1.293 0.197
BMI -0.007313 0.017749 -0.412 0.681
CAD_history 0.094211 0.138611 0.680 0.497
Stroke_history 0.075899 0.130234 0.583 0.560
Peripheral.interv 0.109728 0.154872 0.709 0.479
stenose50-70% -0.898824 0.820875 -1.095 0.274
stenose70-90% -1.030382 0.767061 -1.343 0.180
stenose90-99% -1.011916 0.762658 -1.327 0.186
stenose100% (Occlusion) -1.493765 1.019983 -1.464 0.144
IL6_pg_ug_2015_LN 0.187771 0.040747 4.608 6.10e-06 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.06 on 289 degrees of freedom
Multiple R-squared: 0.09795, Adjusted R-squared: 0.04177
F-statistic: 1.743 on 18 and 289 DF, p-value: 0.03197
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_LN ' with ' Macrophages_LN ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_LN
Trait/outcome.............: Macrophages_LN
Effect size...............: -0.021467
Standard error............: 0.030894
Odds ratio (effect size)..: 0.979
Lower 95% CI..............: 0.921
Upper 95% CI..............: 1.04
T-value...................: -0.694856
P-value...................: 0.4877039
R^2.......................: 0.097948
Adjusted r^2..............: 0.041765
Sample size of AE DB......: 2388
Sample size of model......: 308
Missing data %............: 87.10218
- processing SMC_LN
Call:
lm(formula = currentDF[, PROTEIN] ~ Med.Statin.LLD + IL6_pg_ug_2015_LN,
data = currentDF)
Coefficients:
(Intercept) Med.Statin.LLDyes IL6_pg_ug_2015_LN
4.6808 -0.1892 0.1643
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose +
IL6_pg_ug_2015_LN, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.4525 -0.7612 0.0033 0.6267 3.9346
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 6.181174 1.230408 5.024 8.78e-07 ***
currentDF[, TRAIT] 0.023514 0.039255 0.599 0.550
Age -0.002751 0.008070 -0.341 0.733
Gendermale -0.155936 0.137818 -1.131 0.259
Hypertension.compositeyes -0.054517 0.188258 -0.290 0.772
DiabetesStatusDiabetes 0.049238 0.154631 0.318 0.750
SmokerCurrentyes -0.086609 0.135319 -0.640 0.523
Med.Statin.LLDyes -0.241771 0.137175 -1.763 0.079 .
Med.all.antiplateletyes -0.221620 0.208851 -1.061 0.289
GFR_MDRD 0.004772 0.003605 1.324 0.187
BMI -0.009630 0.016987 -0.567 0.571
CAD_history 0.080537 0.136960 0.588 0.557
Stroke_history 0.039877 0.126630 0.315 0.753
Peripheral.interv 0.134259 0.154797 0.867 0.386
stenose50-70% -0.929859 0.809564 -1.149 0.252
stenose70-90% -1.038246 0.756292 -1.373 0.171
stenose90-99% -1.015694 0.752079 -1.351 0.178
stenose100% (Occlusion) -1.518787 1.002792 -1.515 0.131
IL6_pg_ug_2015_LN 0.171314 0.039323 4.357 1.82e-05 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.046 on 296 degrees of freedom
Multiple R-squared: 0.09119, Adjusted R-squared: 0.03592
F-statistic: 1.65 on 18 and 296 DF, p-value: 0.04768
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_LN ' with ' SMC_LN ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_LN
Trait/outcome.............: SMC_LN
Effect size...............: 0.023514
Standard error............: 0.039255
Odds ratio (effect size)..: 1.024
Lower 95% CI..............: 0.948
Upper 95% CI..............: 1.106
T-value...................: 0.59901
P-value...................: 0.5496245
R^2.......................: 0.091189
Adjusted r^2..............: 0.035923
Sample size of AE DB......: 2388
Sample size of model......: 315
Missing data %............: 86.80904
- processing VesselDensity_LN
Call:
lm(formula = currentDF[, PROTEIN] ~ GFR_MDRD + IL6_pg_ug_2015_LN,
data = currentDF)
Coefficients:
(Intercept) GFR_MDRD IL6_pg_ug_2015_LN
4.194942 0.004928 0.165697
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose +
IL6_pg_ug_2015_LN, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.3449 -0.7867 -0.0304 0.6422 4.0349
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 6.225614 1.280705 4.861 1.92e-06 ***
currentDF[, TRAIT] -0.096433 0.098811 -0.976 0.330
Age -0.003013 0.008161 -0.369 0.712
Gendermale -0.127779 0.139441 -0.916 0.360
Hypertension.compositeyes -0.004603 0.195515 -0.024 0.981
DiabetesStatusDiabetes 0.088105 0.160926 0.547 0.584
SmokerCurrentyes -0.057679 0.139138 -0.415 0.679
Med.Statin.LLDyes -0.228912 0.139883 -1.636 0.103
Med.all.antiplateletyes -0.219084 0.214922 -1.019 0.309
GFR_MDRD 0.005865 0.003695 1.587 0.114
BMI -0.010273 0.017341 -0.592 0.554
CAD_history 0.108852 0.140247 0.776 0.438
Stroke_history 0.077217 0.129761 0.595 0.552
Peripheral.interv 0.155096 0.157608 0.984 0.326
stenose50-70% -0.931291 0.820260 -1.135 0.257
stenose70-90% -1.066183 0.767461 -1.389 0.166
stenose90-99% -1.047425 0.762490 -1.374 0.171
stenose100% (Occlusion) -1.542241 1.019750 -1.512 0.132
IL6_pg_ug_2015_LN 0.169171 0.039667 4.265 2.71e-05 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.059 on 289 degrees of freedom
Multiple R-squared: 0.09279, Adjusted R-squared: 0.03629
F-statistic: 1.642 on 18 and 289 DF, p-value: 0.04945
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_LN ' with ' VesselDensity_LN ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_LN
Trait/outcome.............: VesselDensity_LN
Effect size...............: -0.096433
Standard error............: 0.098811
Odds ratio (effect size)..: 0.908
Lower 95% CI..............: 0.748
Upper 95% CI..............: 1.102
T-value...................: -0.975931
P-value...................: 0.3299147
R^2.......................: 0.092794
Adjusted r^2..............: 0.03629
Sample size of AE DB......: 2388
Sample size of model......: 308
Missing data %............: 87.10218
Analysis of MCP1_LN.
- processing Macrophages_LN
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + DiabetesStatus +
Med.Statin.LLD + CAD_history + Peripheral.interv + IL6_pg_ug_2015_LN,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] DiabetesStatusDiabetes Med.Statin.LLDyes CAD_history
5.42282 0.03618 -0.19017 -0.21111 0.18964
Peripheral.interv IL6_pg_ug_2015_LN
-0.19098 0.17093
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose +
IL6_pg_ug_2015_LN, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.0894 -0.5107 0.0377 0.6253 2.1622
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 6.5922102 0.8650138 7.621 2.24e-13 ***
currentDF[, TRAIT] 0.0325660 0.0220355 1.478 0.1403
Age -0.0083277 0.0054360 -1.532 0.1264
Gendermale 0.1385775 0.0943481 1.469 0.1428
Hypertension.compositeyes -0.1510837 0.1290660 -1.171 0.2425
DiabetesStatusDiabetes -0.1665340 0.1070314 -1.556 0.1206
SmokerCurrentyes -0.0779224 0.0928753 -0.839 0.4020
Med.Statin.LLDyes -0.2435613 0.0983058 -2.478 0.0137 *
Med.all.antiplateletyes 0.1077614 0.1519566 0.709 0.4787
GFR_MDRD -0.0009545 0.0023944 -0.399 0.6904
BMI -0.0130165 0.0117355 -1.109 0.2681
CAD_history 0.1775369 0.0964592 1.841 0.0665 .
Stroke_history 0.0196930 0.0890960 0.221 0.8252
Peripheral.interv -0.1903656 0.1107707 -1.719 0.0866 .
stenose50-70% -0.2004232 0.6205990 -0.323 0.7469
stenose70-90% -0.2165956 0.5830618 -0.371 0.7105
stenose90-99% -0.1922047 0.5805857 -0.331 0.7408
stenose100% (Occlusion) -1.2943097 0.7227984 -1.791 0.0742 .
IL6_pg_ug_2015_LN 0.1735288 0.0274057 6.332 7.20e-10 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.81 on 361 degrees of freedom
Multiple R-squared: 0.1632, Adjusted R-squared: 0.1215
F-statistic: 3.911 on 18 and 361 DF, p-value: 2.367e-07
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_LN ' with ' Macrophages_LN ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_LN
Trait/outcome.............: Macrophages_LN
Effect size...............: 0.032566
Standard error............: 0.022036
Odds ratio (effect size)..: 1.033
Lower 95% CI..............: 0.989
Upper 95% CI..............: 1.079
T-value...................: 1.477887
P-value...................: 0.1403099
R^2.......................: 0.163189
Adjusted r^2..............: 0.121464
Sample size of AE DB......: 2388
Sample size of model......: 380
Missing data %............: 84.0871
- processing SMC_LN
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + Med.Statin.LLD +
Med.all.antiplatelet + CAD_history + Peripheral.interv +
IL6_pg_ug_2015_LN, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age Gendermale Hypertension.compositeyes
5.82157 -0.08187 -0.01093 0.14979 -0.16992
DiabetesStatusDiabetes Med.Statin.LLDyes Med.all.antiplateletyes CAD_history Peripheral.interv
-0.18956 -0.17667 0.23118 0.22428 -0.16555
IL6_pg_ug_2015_LN
0.14869
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose +
IL6_pg_ug_2015_LN, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.06735 -0.55452 -0.00776 0.57409 2.18813
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 6.681140 0.844925 7.907 3.10e-14 ***
currentDF[, TRAIT] -0.082710 0.026881 -3.077 0.00225 **
Age -0.013151 0.005325 -2.470 0.01397 *
Gendermale 0.142034 0.092428 1.537 0.12523
Hypertension.compositeyes -0.182526 0.123943 -1.473 0.14170
DiabetesStatusDiabetes -0.180275 0.104594 -1.724 0.08562 .
SmokerCurrentyes -0.086804 0.089914 -0.965 0.33497
Med.Statin.LLDyes -0.194348 0.095049 -2.045 0.04159 *
Med.all.antiplateletyes 0.140214 0.147589 0.950 0.34272
GFR_MDRD -0.001075 0.002351 -0.457 0.64778
BMI -0.011283 0.011311 -0.998 0.31917
CAD_history 0.208520 0.095341 2.187 0.02936 *
Stroke_history 0.038319 0.086917 0.441 0.65957
Peripheral.interv -0.182971 0.109885 -1.665 0.09674 .
stenose50-70% -0.144584 0.612292 -0.236 0.81346
stenose70-90% -0.199810 0.575211 -0.347 0.72851
stenose90-99% -0.173815 0.572786 -0.303 0.76171
stenose100% (Occlusion) -1.254017 0.712227 -1.761 0.07912 .
IL6_pg_ug_2015_LN 0.150253 0.026926 5.580 4.67e-08 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.7993 on 368 degrees of freedom
Multiple R-squared: 0.1799, Adjusted R-squared: 0.1398
F-statistic: 4.485 on 18 and 368 DF, p-value: 7.585e-09
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_LN ' with ' SMC_LN ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_LN
Trait/outcome.............: SMC_LN
Effect size...............: -0.08271
Standard error............: 0.026881
Odds ratio (effect size)..: 0.921
Lower 95% CI..............: 0.873
Upper 95% CI..............: 0.97
T-value...................: -3.076904
P-value...................: 0.002248252
R^2.......................: 0.179909
Adjusted r^2..............: 0.139796
Sample size of AE DB......: 2388
Sample size of model......: 387
Missing data %............: 83.79397
- processing VesselDensity_LN
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + DiabetesStatus + Med.Statin.LLD + Med.all.antiplatelet +
CAD_history + IL6_pg_ug_2015_LN, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age Gendermale DiabetesStatusDiabetes
5.662582 -0.102797 -0.007737 0.233123 -0.157455
Med.Statin.LLDyes Med.all.antiplateletyes CAD_history IL6_pg_ug_2015_LN
-0.194810 0.255192 0.196574 0.172016
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose +
IL6_pg_ug_2015_LN, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.3634 -0.5541 0.0188 0.6013 2.2016
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 6.619759 0.872874 7.584 2.89e-13 ***
currentDF[, TRAIT] -0.115158 0.063779 -1.806 0.0718 .
Age -0.009234 0.005326 -1.734 0.0838 .
Gendermale 0.217503 0.093097 2.336 0.0200 *
Hypertension.compositeyes -0.154299 0.128341 -1.202 0.2301
DiabetesStatusDiabetes -0.137882 0.106947 -1.289 0.1981
SmokerCurrentyes -0.098746 0.091868 -1.075 0.2832
Med.Statin.LLDyes -0.201521 0.096729 -2.083 0.0379 *
Med.all.antiplateletyes 0.159414 0.150841 1.057 0.2913
GFR_MDRD -0.000691 0.002419 -0.286 0.7753
BMI -0.011145 0.011563 -0.964 0.3358
CAD_history 0.207952 0.097714 2.128 0.0340 *
Stroke_history 0.041639 0.089080 0.467 0.6405
Peripheral.interv -0.138894 0.112537 -1.234 0.2179
stenose50-70% -0.225423 0.617761 -0.365 0.7154
stenose70-90% -0.213750 0.580972 -0.368 0.7132
stenose90-99% -0.178468 0.578172 -0.309 0.7577
stenose100% (Occlusion) -1.264080 0.720276 -1.755 0.0801 .
IL6_pg_ug_2015_LN 0.174170 0.026779 6.504 2.62e-10 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8061 on 360 degrees of freedom
Multiple R-squared: 0.1713, Adjusted R-squared: 0.1299
F-statistic: 4.135 on 18 and 360 DF, p-value: 6.343e-08
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_LN ' with ' VesselDensity_LN ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_LN
Trait/outcome.............: VesselDensity_LN
Effect size...............: -0.115159
Standard error............: 0.063779
Odds ratio (effect size)..: 0.891
Lower 95% CI..............: 0.786
Upper 95% CI..............: 1.01
T-value...................: -1.805579
P-value...................: 0.07181944
R^2.......................: 0.171328
Adjusted r^2..............: 0.129895
Sample size of AE DB......: 2388
Sample size of model......: 379
Missing data %............: 84.12898
Analysis of IL6_pg_ug_2015_LN.
- processing Macrophages_LNattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsense
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Hypertension.composite + SmokerCurrent + Med.Statin.LLD +
Med.all.antiplatelet + GFR_MDRD + BMI + CAD_history + Stroke_history +
Peripheral.interv + stenose + IL6_pg_ug_2015_LN, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age Hypertension.compositeyes SmokerCurrentyes
0 0 0 0 0
Med.Statin.LLDyes Med.all.antiplateletyes GFR_MDRD BMI CAD_history
0 0 0 0 0
Stroke_history Peripheral.interv stenose50-70% stenose70-90% stenose90-99%
0 0 0 0 0
stenose100% (Occlusion) stenose50-99% stenose70-99% IL6_pg_ug_2015_LN
0 0 0 1 essentially perfect fit: summary may be unreliable
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose +
IL6_pg_ug_2015_LN, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-9.364e-16 -2.744e-17 1.700e-19 2.931e-17 2.894e-16
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.000e+00 4.875e-17 0.000e+00 1
currentDF[, TRAIT] 0.000e+00 1.348e-18 0.000e+00 1
Age 0.000e+00 3.016e-19 0.000e+00 1
Gendermale 0.000e+00 5.374e-18 0.000e+00 1
Hypertension.compositeyes 0.000e+00 7.505e-18 0.000e+00 1
DiabetesStatusDiabetes 0.000e+00 5.875e-18 0.000e+00 1
SmokerCurrentyes 0.000e+00 5.417e-18 0.000e+00 1
Med.Statin.LLDyes 0.000e+00 5.946e-18 0.000e+00 1
Med.all.antiplateletyes 0.000e+00 8.002e-18 0.000e+00 1
GFR_MDRD 0.000e+00 1.280e-19 0.000e+00 1
BMI 0.000e+00 6.748e-19 0.000e+00 1
CAD_history 0.000e+00 5.515e-18 0.000e+00 1
Stroke_history 0.000e+00 5.200e-18 0.000e+00 1
Peripheral.interv 0.000e+00 6.415e-18 0.000e+00 1
stenose50-70% 0.000e+00 3.494e-17 0.000e+00 1
stenose70-90% 0.000e+00 3.375e-17 0.000e+00 1
stenose90-99% 0.000e+00 3.374e-17 0.000e+00 1
stenose100% (Occlusion) 0.000e+00 4.412e-17 0.000e+00 1
stenose50-99% 0.000e+00 6.266e-17 0.000e+00 1
stenose70-99% 0.000e+00 4.545e-17 0.000e+00 1
IL6_pg_ug_2015_LN 1.000e+00 1.682e-18 5.944e+17 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 7.474e-17 on 955 degrees of freedom
Multiple R-squared: 1, Adjusted R-squared: 1
F-statistic: 1.857e+34 on 20 and 955 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_LN ' with ' Macrophages_LN ' .essentially perfect fit: summary may be unreliableCollecting data.essentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableWe have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_pg_ug_2015_LN
Trait/outcome.............: Macrophages_LN
Effect size...............: 0
Standard error............: 0
Odds ratio (effect size)..: 1
Lower 95% CI..............: 1
Upper 95% CI..............: 1
T-value...................: 0
P-value...................: 1
R^2.......................: 1
Adjusted r^2..............: 1
Sample size of AE DB......: 2388
Sample size of model......: 976
Missing data %............: 59.12898
- processing SMC_LNattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsense
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
DiabetesStatus + SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet +
BMI + Stroke_history + Peripheral.interv + stenose + IL6_pg_ug_2015_LN +
CAD_history, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age DiabetesStatusDiabetes SmokerCurrentyes
0.000e+00 0.000e+00 0.000e+00 0.000e+00 0.000e+00
Med.Statin.LLDyes Med.all.antiplateletyes BMI Stroke_history Peripheral.interv
0.000e+00 0.000e+00 0.000e+00 0.000e+00 0.000e+00
stenose50-70% stenose70-90% stenose90-99% stenose100% (Occlusion) stenose50-99%
0.000e+00 0.000e+00 0.000e+00 0.000e+00 0.000e+00
stenose70-99% IL6_pg_ug_2015_LN CAD_history
0.000e+00 1.000e+00 -7.715e-18 essentially perfect fit: summary may be unreliable
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose +
IL6_pg_ug_2015_LN, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-9.157e-15 -3.370e-17 6.900e-18 4.980e-17 4.712e-16
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.000e+00 1.998e-16 0.000e+00 1
currentDF[, TRAIT] 0.000e+00 6.793e-18 0.000e+00 1
Age 0.000e+00 1.239e-18 0.000e+00 1
Gendermale 0.000e+00 2.212e-17 0.000e+00 1
Hypertension.compositeyes 0.000e+00 3.043e-17 0.000e+00 1
DiabetesStatusDiabetes 0.000e+00 2.413e-17 0.000e+00 1
SmokerCurrentyes 0.000e+00 2.219e-17 0.000e+00 1
Med.Statin.LLDyes 0.000e+00 2.412e-17 0.000e+00 1
Med.all.antiplateletyes 0.000e+00 3.275e-17 0.000e+00 1
GFR_MDRD 0.000e+00 5.282e-19 0.000e+00 1
BMI 0.000e+00 2.745e-18 0.000e+00 1
CAD_history 0.000e+00 2.271e-17 0.000e+00 1
Stroke_history 0.000e+00 2.128e-17 0.000e+00 1
Peripheral.interv 0.000e+00 2.637e-17 0.000e+00 1
stenose50-70% 0.000e+00 1.438e-16 0.000e+00 1
stenose70-90% 0.000e+00 1.390e-16 0.000e+00 1
stenose90-99% 0.000e+00 1.389e-16 0.000e+00 1
stenose100% (Occlusion) 0.000e+00 1.767e-16 0.000e+00 1
stenose50-99% 0.000e+00 2.582e-16 0.000e+00 1
stenose70-99% 0.000e+00 1.872e-16 0.000e+00 1
IL6_pg_ug_2015_LN 1.000e+00 6.882e-18 1.453e+17 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 3.077e-16 on 965 degrees of freedom
Multiple R-squared: 1, Adjusted R-squared: 1
F-statistic: 1.117e+33 on 20 and 965 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_LN ' with ' SMC_LN ' .essentially perfect fit: summary may be unreliableCollecting data.essentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableWe have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_pg_ug_2015_LN
Trait/outcome.............: SMC_LN
Effect size...............: 0
Standard error............: 0
Odds ratio (effect size)..: 1
Lower 95% CI..............: 1
Upper 95% CI..............: 1
T-value...................: 0
P-value...................: 1
R^2.......................: 1
Adjusted r^2..............: 1
Sample size of AE DB......: 2388
Sample size of model......: 986
Missing data %............: 58.71022
- processing VesselDensity_LNattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsense
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + stenose + IL6_pg_ug_2015_LN,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age Gendermale Hypertension.compositeyes
0 0 0 0 0
DiabetesStatusDiabetes SmokerCurrentyes Med.Statin.LLDyes Med.all.antiplateletyes GFR_MDRD
0 0 0 0 0
BMI CAD_history Stroke_history stenose50-70% stenose70-90%
0 0 0 0 0
stenose90-99% stenose100% (Occlusion) stenose50-99% stenose70-99% IL6_pg_ug_2015_LN
0 0 0 0 1 essentially perfect fit: summary may be unreliable
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose +
IL6_pg_ug_2015_LN, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.969e-16 -2.654e-17 1.430e-18 2.794e-17 3.111e-16
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.000e+00 4.555e-17 0.000e+00 1
currentDF[, TRAIT] 0.000e+00 2.672e-18 0.000e+00 1
Age 0.000e+00 2.841e-19 0.000e+00 1
Gendermale 0.000e+00 5.085e-18 0.000e+00 1
Hypertension.compositeyes 0.000e+00 7.075e-18 0.000e+00 1
DiabetesStatusDiabetes 0.000e+00 5.696e-18 0.000e+00 1
SmokerCurrentyes 0.000e+00 5.167e-18 0.000e+00 1
Med.Statin.LLDyes 0.000e+00 5.575e-18 0.000e+00 1
Med.all.antiplateletyes 0.000e+00 7.841e-18 0.000e+00 1
GFR_MDRD 0.000e+00 1.234e-19 0.000e+00 1
BMI 0.000e+00 6.333e-19 0.000e+00 1
CAD_history 0.000e+00 5.331e-18 0.000e+00 1
Stroke_history 0.000e+00 4.953e-18 0.000e+00 1
Peripheral.interv 0.000e+00 6.250e-18 0.000e+00 1
stenose50-70% 0.000e+00 3.205e-17 0.000e+00 1
stenose70-90% 0.000e+00 3.076e-17 0.000e+00 1
stenose90-99% 0.000e+00 3.072e-17 0.000e+00 1
stenose100% (Occlusion) 0.000e+00 4.017e-17 0.000e+00 1
stenose50-99% 0.000e+00 5.700e-17 0.000e+00 1
stenose70-99% 0.000e+00 4.583e-17 0.000e+00 1
IL6_pg_ug_2015_LN 1.000e+00 1.580e-18 6.327e+17 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 6.797e-17 on 869 degrees of freedom
Multiple R-squared: 1, Adjusted R-squared: 1
F-statistic: 2.083e+34 on 20 and 869 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_LN ' with ' VesselDensity_LN ' .essentially perfect fit: summary may be unreliableCollecting data.essentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableWe have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_pg_ug_2015_LN
Trait/outcome.............: VesselDensity_LN
Effect size...............: 0
Standard error............: 0
Odds ratio (effect size)..: 1
Lower 95% CI..............: 1
Upper 95% CI..............: 1
T-value...................: 0
P-value...................: 1
R^2.......................: 1
Adjusted r^2..............: 1
Sample size of AE DB......: 2388
Sample size of model......: 890
Missing data %............: 62.73032
Analysis of IL6R_pg_ug_2015_LN.
- processing Macrophages_LN
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Med.Statin.LLD + Peripheral.interv + stenose + IL6_pg_ug_2015_LN,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age Gendermale Med.Statin.LLDyes
-1.242877 0.056355 -0.006051 -0.127290 -0.343429
Peripheral.interv stenose50-70% stenose70-90% stenose90-99% stenose100% (Occlusion)
0.267173 0.649136 0.905228 1.038322 0.609550
stenose50-99% stenose70-99% IL6_pg_ug_2015_LN
0.884428 -0.025479 0.285405
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose +
IL6_pg_ug_2015_LN, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-5.9558 -0.5150 0.1072 0.6086 2.9779
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.966276 0.715026 -1.351 0.17690
currentDF[, TRAIT] 0.055979 0.018885 2.964 0.00311 **
Age -0.007873 0.004258 -1.849 0.06476 .
Gendermale -0.100562 0.075655 -1.329 0.18411
Hypertension.compositeyes 0.102874 0.104214 0.987 0.32384
DiabetesStatusDiabetes -0.105455 0.082109 -1.284 0.19935
SmokerCurrentyes -0.007566 0.075970 -0.100 0.92069
Med.Statin.LLDyes -0.342999 0.083058 -4.130 3.96e-05 ***
Med.all.antiplateletyes 0.061685 0.111912 0.551 0.58164
GFR_MDRD -0.002185 0.001823 -1.199 0.23093
BMI -0.006955 0.009654 -0.720 0.47143
CAD_history -0.045923 0.077279 -0.594 0.55249
Stroke_history 0.046251 0.072628 0.637 0.52440
Peripheral.interv 0.275802 0.090336 3.053 0.00233 **
stenose50-70% 0.687748 0.534119 1.288 0.19820
stenose70-90% 0.943458 0.518762 1.819 0.06929 .
stenose90-99% 1.071486 0.518557 2.066 0.03908 *
stenose100% (Occlusion) 0.650093 0.648653 1.002 0.31650
stenose50-99% 0.877489 0.891309 0.984 0.32513
stenose70-99% -0.006258 0.666402 -0.009 0.99251
IL6_pg_ug_2015_LN 0.282153 0.023495 12.009 < 2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.027 on 922 degrees of freedom
Multiple R-squared: 0.2018, Adjusted R-squared: 0.1845
F-statistic: 11.65 on 20 and 922 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_LN ' with ' Macrophages_LN ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6R_pg_ug_2015_LN
Trait/outcome.............: Macrophages_LN
Effect size...............: 0.055979
Standard error............: 0.018885
Odds ratio (effect size)..: 1.058
Lower 95% CI..............: 1.019
Upper 95% CI..............: 1.097
T-value...................: 2.964161
P-value...................: 0.00311319
R^2.......................: 0.201768
Adjusted r^2..............: 0.184452
Sample size of AE DB......: 2388
Sample size of model......: 943
Missing data %............: 60.51089
- processing SMC_LN
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
DiabetesStatus + Med.Statin.LLD + GFR_MDRD + Peripheral.interv +
stenose + IL6_pg_ug_2015_LN, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age DiabetesStatusDiabetes Med.Statin.LLDyes
-1.112015 0.097459 -0.006820 -0.119368 -0.333161
GFR_MDRD Peripheral.interv stenose50-70% stenose70-90% stenose90-99%
-0.002565 0.268643 0.679203 0.926560 1.056767
stenose100% (Occlusion) stenose50-99% stenose70-99% IL6_pg_ug_2015_LN
0.534907 0.747309 -0.034556 0.301993
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose +
IL6_pg_ug_2015_LN, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-6.1096 -0.5523 0.0949 0.6268 3.1080
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.149126 0.704847 -1.630 0.1034
currentDF[, TRAIT] 0.095663 0.023010 4.157 3.51e-05 ***
Age -0.007165 0.004206 -1.703 0.0888 .
Gendermale -0.025751 0.074756 -0.344 0.7306
Hypertension.compositeyes 0.089848 0.101636 0.884 0.3769
DiabetesStatusDiabetes -0.115065 0.081121 -1.418 0.1564
SmokerCurrentyes -0.009264 0.074833 -0.124 0.9015
Med.Statin.LLDyes -0.334934 0.080984 -4.136 3.86e-05 ***
Med.all.antiplateletyes 0.067913 0.110179 0.616 0.5378
GFR_MDRD -0.002576 0.001810 -1.423 0.1550
BMI -0.002947 0.009437 -0.312 0.7549
CAD_history -0.048394 0.076543 -0.632 0.5274
Stroke_history 0.055612 0.071499 0.778 0.4369
Peripheral.interv 0.275458 0.089322 3.084 0.0021 **
stenose50-70% 0.684789 0.528754 1.295 0.1956
stenose70-90% 0.937515 0.513760 1.825 0.0683 .
stenose90-99% 1.063799 0.513562 2.071 0.0386 *
stenose100% (Occlusion) 0.572933 0.627488 0.913 0.3614
stenose50-99% 0.751427 0.883539 0.850 0.3953
stenose70-99% -0.031342 0.660105 -0.047 0.9621
IL6_pg_ug_2015_LN 0.299518 0.023167 12.928 < 2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.017 on 932 degrees of freedom
Multiple R-squared: 0.2084, Adjusted R-squared: 0.1914
F-statistic: 12.27 on 20 and 932 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_LN ' with ' SMC_LN ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6R_pg_ug_2015_LN
Trait/outcome.............: SMC_LN
Effect size...............: 0.095663
Standard error............: 0.02301
Odds ratio (effect size)..: 1.1
Lower 95% CI..............: 1.052
Upper 95% CI..............: 1.151
T-value...................: 4.157498
P-value...................: 3.514086e-05
R^2.......................: 0.208431
Adjusted r^2..............: 0.191445
Sample size of AE DB......: 2388
Sample size of model......: 953
Missing data %............: 60.09213
- processing VesselDensity_LN
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
DiabetesStatus + Med.Statin.LLD + Peripheral.interv + stenose +
IL6_pg_ug_2015_LN, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age DiabetesStatusDiabetes Med.Statin.LLDyes
-1.417139 0.100429 -0.009354 -0.155244 -0.374452
Peripheral.interv stenose50-70% stenose70-90% stenose90-99% stenose100% (Occlusion)
0.280068 0.737543 1.050648 1.157583 0.634774
stenose50-99% stenose70-99% IL6_pg_ug_2015_LN
0.845134 0.159239 0.287450
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose +
IL6_pg_ug_2015_LN, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-5.9547 -0.5083 0.0729 0.5954 2.9537
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.059091 0.743676 -1.424 0.1548
currentDF[, TRAIT] 0.101026 0.042011 2.405 0.0164 *
Age -0.011355 0.004466 -2.543 0.0112 *
Gendermale -0.077358 0.079545 -0.973 0.3311
Hypertension.compositeyes 0.106818 0.109344 0.977 0.3289
DiabetesStatusDiabetes -0.154746 0.088733 -1.744 0.0815 .
SmokerCurrentyes -0.031953 0.080677 -0.396 0.6922
Med.Statin.LLDyes -0.375621 0.086700 -4.332 1.65e-05 ***
Med.all.antiplateletyes 0.048492 0.122309 0.396 0.6919
GFR_MDRD -0.002204 0.001966 -1.121 0.2627
BMI -0.006095 0.010100 -0.603 0.5464
CAD_history -0.088731 0.083166 -1.067 0.2863
Stroke_history 0.034438 0.077028 0.447 0.6549
Peripheral.interv 0.276295 0.097946 2.821 0.0049 **
stenose50-70% 0.770678 0.544201 1.416 0.1571
stenose70-90% 1.081230 0.525369 2.058 0.0399 *
stenose90-99% 1.183737 0.524757 2.256 0.0243 *
stenose100% (Occlusion) 0.703847 0.656668 1.072 0.2841
stenose50-99% 0.897784 0.901672 0.996 0.3197
stenose70-99% 0.192480 0.738956 0.260 0.7946
IL6_pg_ug_2015_LN 0.284042 0.024577 11.557 < 2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.039 on 837 degrees of freedom
Multiple R-squared: 0.1997, Adjusted R-squared: 0.1806
F-statistic: 10.45 on 20 and 837 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_LN ' with ' VesselDensity_LN ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6R_pg_ug_2015_LN
Trait/outcome.............: VesselDensity_LN
Effect size...............: 0.101026
Standard error............: 0.042011
Odds ratio (effect size)..: 1.106
Lower 95% CI..............: 1.019
Upper 95% CI..............: 1.201
T-value...................: 2.404755
P-value...................: 0.01639953
R^2.......................: 0.199738
Adjusted r^2..............: 0.180616
Sample size of AE DB......: 2388
Sample size of model......: 858
Missing data %............: 64.07035
Analysis of MCP1_pg_ug_2015_LN.
- processing Macrophages_LN
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Med.Statin.LLD +
stenose + IL6_pg_ug_2015_LN, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Med.Statin.LLDyes stenose50-70% stenose70-90%
-0.06925 -0.06816 -0.14401 0.41560 0.37828
stenose90-99% stenose100% (Occlusion) stenose50-99% stenose70-99% IL6_pg_ug_2015_LN
0.20462 -0.82420 1.06365 0.78594 0.43860
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose +
IL6_pg_ug_2015_LN, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.9520 -0.7606 -0.0173 0.8465 3.4182
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.0163763 0.7449520 -0.022 0.982466
currentDF[, TRAIT] -0.0688244 0.0205939 -3.342 0.000864 ***
Age 0.0013390 0.0046147 0.290 0.771754
Gendermale 0.0813908 0.0821211 0.991 0.321885
Hypertension.compositeyes -0.1421067 0.1146687 -1.239 0.215546
DiabetesStatusDiabetes -0.0367938 0.0897758 -0.410 0.682015
SmokerCurrentyes -0.0469934 0.0827910 -0.568 0.570430
Med.Statin.LLDyes -0.1426960 0.0911033 -1.566 0.117607
Med.all.antiplateletyes -0.0340903 0.1222730 -0.279 0.780455
GFR_MDRD 0.0002204 0.0019577 0.113 0.910382
BMI -0.0020417 0.0103105 -0.198 0.843073
CAD_history 0.0283634 0.0843530 0.336 0.736759
Stroke_history 0.0501069 0.0794523 0.631 0.528418
Peripheral.interv 0.1051598 0.0980445 1.073 0.283735
stenose50-70% 0.3655695 0.5338315 0.685 0.493634
stenose70-90% 0.3468000 0.5157343 0.672 0.501467
stenose90-99% 0.1783612 0.5154914 0.346 0.729417
stenose100% (Occlusion) -0.8757534 0.6741363 -1.299 0.194232
stenose50-99% 1.0443032 0.9573696 1.091 0.275634
stenose70-99% 0.7383695 0.6944600 1.063 0.287947
IL6_pg_ug_2015_LN 0.4385550 0.0258285 16.979 < 2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.142 on 954 degrees of freedom
Multiple R-squared: 0.2511, Adjusted R-squared: 0.2354
F-statistic: 15.99 on 20 and 954 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_LN ' with ' Macrophages_LN ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_LN
Trait/outcome.............: Macrophages_LN
Effect size...............: -0.068824
Standard error............: 0.020594
Odds ratio (effect size)..: 0.933
Lower 95% CI..............: 0.897
Upper 95% CI..............: 0.972
T-value...................: -3.341973
P-value...................: 0.000864164
R^2.......................: 0.251058
Adjusted r^2..............: 0.235357
Sample size of AE DB......: 2388
Sample size of model......: 975
Missing data %............: 59.17085
- processing SMC_LN
Call:
lm(formula = currentDF[, PROTEIN] ~ Med.Statin.LLD + stenose +
IL6_pg_ug_2015_LN, data = currentDF)
Coefficients:
(Intercept) Med.Statin.LLDyes stenose50-70% stenose70-90% stenose90-99%
0.009487 -0.164150 0.412654 0.355694 0.189719
stenose100% (Occlusion) stenose50-99% stenose70-99% IL6_pg_ug_2015_LN
-0.600048 1.032517 0.768769 0.423669
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose +
IL6_pg_ug_2015_LN, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.8483 -0.7505 -0.0237 0.8525 3.3474
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.0325248 0.7466028 -0.044 0.9653
currentDF[, TRAIT] -0.0139722 0.0253879 -0.550 0.5822
Age 0.0025893 0.0046357 0.559 0.5766
Gendermale 0.0688454 0.0826644 0.833 0.4051
Hypertension.compositeyes -0.1507656 0.1136938 -1.326 0.1851
DiabetesStatusDiabetes -0.0656779 0.0901625 -0.728 0.4665
SmokerCurrentyes -0.0363466 0.0829328 -0.438 0.6613
Med.Statin.LLDyes -0.1625254 0.0903411 -1.799 0.0723 .
Med.all.antiplateletyes -0.0230150 0.1223899 -0.188 0.8509
GFR_MDRD 0.0005668 0.0019755 0.287 0.7743
BMI -0.0024383 0.0102576 -0.238 0.8122
CAD_history 0.0296729 0.0849242 0.349 0.7269
Stroke_history 0.0269355 0.0795241 0.339 0.7349
Peripheral.interv 0.1480121 0.0985442 1.502 0.1334
stenose50-70% 0.3622053 0.5371766 0.674 0.5003
stenose70-90% 0.3267152 0.5193234 0.629 0.5294
stenose90-99% 0.1660522 0.5191143 0.320 0.7491
stenose100% (Occlusion) -0.6657976 0.6600302 -1.009 0.3134
stenose50-99% 1.0441546 0.9646197 1.082 0.2793
stenose70-99% 0.7257635 0.6993507 1.038 0.2996
IL6_pg_ug_2015_LN 0.4232919 0.0258245 16.391 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.15 on 964 degrees of freedom
Multiple R-squared: 0.2402, Adjusted R-squared: 0.2244
F-statistic: 15.23 on 20 and 964 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_LN ' with ' SMC_LN ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_LN
Trait/outcome.............: SMC_LN
Effect size...............: -0.013972
Standard error............: 0.025388
Odds ratio (effect size)..: 0.986
Lower 95% CI..............: 0.938
Upper 95% CI..............: 1.036
T-value...................: -0.550351
P-value...................: 0.5822061
R^2.......................: 0.240151
Adjusted r^2..............: 0.224387
Sample size of AE DB......: 2388
Sample size of model......: 985
Missing data %............: 58.75209
- processing VesselDensity_LN
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
Med.Statin.LLD + IL6_pg_ug_2015_LN, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale Med.Statin.LLDyes IL6_pg_ug_2015_LN
0.4345 -0.1410 0.1228 -0.1753 0.4234
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose +
IL6_pg_ug_2015_LN, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.7823 -0.7462 -0.0609 0.8324 3.4019
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.809e-01 7.789e-01 0.489 0.62491
currentDF[, TRAIT] -1.340e-01 4.572e-02 -2.931 0.00347 **
Age 1.477e-03 4.863e-03 0.304 0.76142
Gendermale 1.221e-01 8.693e-02 1.405 0.16051
Hypertension.compositeyes -1.576e-01 1.210e-01 -1.303 0.19295
DiabetesStatusDiabetes -3.504e-02 9.739e-02 -0.360 0.71912
SmokerCurrentyes -4.309e-02 8.835e-02 -0.488 0.62582
Med.Statin.LLDyes -1.778e-01 9.557e-02 -1.860 0.06321 .
Med.all.antiplateletyes 4.854e-02 1.341e-01 0.362 0.71739
GFR_MDRD -6.416e-05 2.113e-03 -0.030 0.97578
BMI -4.015e-03 1.083e-02 -0.371 0.71082
CAD_history 5.405e-02 9.124e-02 0.592 0.55375
Stroke_history 3.583e-02 8.467e-02 0.423 0.67226
Peripheral.interv 8.863e-02 1.069e-01 0.829 0.40716
stenose50-70% 2.078e-01 5.479e-01 0.379 0.70463
stenose70-90% 2.034e-01 5.259e-01 0.387 0.69900
stenose90-99% 7.189e-02 5.252e-01 0.137 0.89115
stenose100% (Occlusion) -9.176e-01 6.867e-01 -1.336 0.18184
stenose50-99% 9.888e-01 9.744e-01 1.015 0.31047
stenose70-99% 4.743e-01 7.835e-01 0.605 0.54506
IL6_pg_ug_2015_LN 4.268e-01 2.716e-02 15.715 < 2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.162 on 868 degrees of freedom
Multiple R-squared: 0.25, Adjusted R-squared: 0.2327
F-statistic: 14.47 on 20 and 868 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_LN ' with ' VesselDensity_LN ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_LN
Trait/outcome.............: VesselDensity_LN
Effect size...............: -0.133995
Standard error............: 0.045718
Odds ratio (effect size)..: 0.875
Lower 95% CI..............: 0.8
Upper 95% CI..............: 0.957
T-value...................: -2.930908
P-value...................: 0.00346839
R^2.......................: 0.250029
Adjusted r^2..............: 0.232749
Sample size of AE DB......: 2388
Sample size of model......: 889
Missing data %............: 62.77219 cat("Edit the column names...\n")Edit the column names...colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
"Beta", "s.e.m.",
"OR", "low95CI", "up95CI",
"T-value", "P-value", "r^2", "r^2_adj", "AE_N", "Model_N", "Perc_Miss")
cat("Correct the variable types...\n")Correct the variable types...GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)
# Save the data
cat("Writing results to Excel-file...\n")Writing results to Excel-file...### Univariate
library(openxlsx)
write.xlsx(GLM.results,
file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Con.Multi.Protein.PlaquePhenotypes.MODEL5.xlsx"),
row.names = FALSE, col.names = TRUE, sheetName = "Con.Multi.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")Removing intermediate files...rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)
Analysis of binary plaque traits as a function of serum/plaque IL6(R)/MCP1 levels.
GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN)) {
PROTEIN = TRAITS.PROTEIN[protein]
cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
for (trait in 1:length(TRAITS.BIN)) {
TRAIT = TRAITS.BIN[trait]
cat(paste0("\n- processing ",TRAIT,"\n\n"))
currentDF <- as.data.frame(AEDB.CEA %>%
dplyr::select(., PROTEIN, TRAIT, COVARIATES_M5) %>%
filter(complete.cases(.))) %>%
filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
# for debug
# print(DT::datatable(currentDF))
# print(nrow(currentDF))
# print(str(currentDF))
# print(class(currentDF[,TRAIT]))
### univariate
fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender +
Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose + IL6_pg_ug_2015_LN,
data = currentDF, family = binomial(link = "logit"))
model_step <- stepAIC(fit, direction = "both", trace = FALSE)
print(model_step)
print(summary(fit))
GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
GLM.results = rbind(GLM.results, GLM.results.TEMP)
}
}
Analysis of IL6_LN.
- processing CalcificationPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Hypertension.composite + DiabetesStatus + GFR_MDRD + Stroke_history,
family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Hypertension.compositeyes DiabetesStatusDiabetes GFR_MDRD
0.12030 0.21230 0.63975 -0.55916 -0.01326
Stroke_history
-0.39735
Degrees of Freedom: 318 Total (i.e. Null); 313 Residual
Null Deviance: 432.7
Residual Deviance: 416.8 AIC: 428.8
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + IL6_pg_ug_2015_LN, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.2221 -1.1884 0.7612 1.0022 1.7701
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.990445 2.534116 0.391 0.6959
currentDF[, PROTEIN] 0.227533 0.119111 1.910 0.0561 .
Age -0.006221 0.016260 -0.383 0.7020
Gendermale -0.392668 0.279558 -1.405 0.1601
Hypertension.compositeyes 0.733341 0.379484 1.932 0.0533 .
DiabetesStatusDiabetes -0.504068 0.309402 -1.629 0.1033
SmokerCurrentyes -0.143862 0.269962 -0.533 0.5941
Med.Statin.LLDyes -0.075632 0.278371 -0.272 0.7859
Med.all.antiplateletyes 0.370670 0.414175 0.895 0.3708
GFR_MDRD -0.015640 0.007490 -2.088 0.0368 *
BMI -0.031275 0.034200 -0.914 0.3605
CAD_history 0.036519 0.274286 0.133 0.8941
Stroke_history -0.420124 0.254378 -1.652 0.0986 .
Peripheral.interv -0.384648 0.304697 -1.262 0.2068
stenose50-70% 0.868301 1.568518 0.554 0.5799
stenose70-90% 0.706068 1.456805 0.485 0.6279
stenose90-99% 0.323228 1.446476 0.223 0.8232
stenose100% (Occlusion) 1.180498 1.998297 0.591 0.5547
IL6_pg_ug_2015_LN -0.010201 0.080798 -0.126 0.8995
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 432.7 on 318 degrees of freedom
Residual deviance: 408.1 on 300 degrees of freedom
AIC: 446.1
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_LN ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_LN
Trait/outcome.............: CalcificationPlaque
Effect size...............: 0.227533
Standard error............: 0.119111
Odds ratio (effect size)..: 1.255
Lower 95% CI..............: 0.994
Upper 95% CI..............: 1.586
Z-value...................: 1.91025
P-value...................: 0.05610103
Hosmer and Lemeshow r^2...: 0.056853
Cox and Snell r^2.........: 0.074218
Nagelkerke's pseudo r^2...: 0.099969
Sample size of AE DB......: 2388
Sample size of model......: 319
Missing data %............: 86.64154
- processing CollagenPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ DiabetesStatus +
Med.all.antiplatelet + stenose, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) DiabetesStatusDiabetes Med.all.antiplateletyes stenose50-70% stenose70-90%
16.84114 0.57077 0.72493 -0.04498 -16.30025
stenose90-99% stenose100% (Occlusion)
-16.48248 0.72493
Degrees of Freedom: 318 Total (i.e. Null); 312 Residual
Null Deviance: 338.2
Residual Deviance: 323.6 AIC: 337.6
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + IL6_pg_ug_2015_LN, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.00741 0.00022 0.61415 0.76055 1.14093
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.772e+01 2.796e+03 0.006 0.9949
currentDF[, PROTEIN] 9.851e-02 1.347e-01 0.731 0.4647
Age 2.759e-03 1.919e-02 0.144 0.8857
Gendermale -3.944e-02 3.230e-01 -0.122 0.9028
Hypertension.compositeyes 3.110e-01 4.299e-01 0.724 0.4694
DiabetesStatusDiabetes 7.254e-01 4.068e-01 1.783 0.0746 .
SmokerCurrentyes 3.079e-01 3.221e-01 0.956 0.3391
Med.Statin.LLDyes -1.470e-03 3.222e-01 -0.005 0.9964
Med.all.antiplateletyes 6.842e-01 4.460e-01 1.534 0.1250
GFR_MDRD -9.908e-03 8.779e-03 -1.129 0.2591
BMI -5.029e-02 4.101e-02 -1.226 0.2201
CAD_history -5.053e-02 3.195e-01 -0.158 0.8743
Stroke_history 3.052e-01 3.125e-01 0.977 0.3287
Peripheral.interv -4.444e-01 3.400e-01 -1.307 0.1911
stenose50-70% -2.758e-02 2.995e+03 0.000 1.0000
stenose70-90% -1.623e+01 2.796e+03 -0.006 0.9954
stenose90-99% -1.648e+01 2.796e+03 -0.006 0.9953
stenose100% (Occlusion) 7.061e-01 3.606e+03 0.000 0.9998
IL6_pg_ug_2015_LN -6.681e-02 9.415e-02 -0.710 0.4780
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 338.23 on 318 degrees of freedom
Residual deviance: 315.17 on 300 degrees of freedom
AIC: 353.17
Number of Fisher Scoring iterations: 16
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_LN ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_LN
Trait/outcome.............: CollagenPlaque
Effect size...............: 0.098509
Standard error............: 0.134721
Odds ratio (effect size)..: 1.104
Lower 95% CI..............: 0.847
Upper 95% CI..............: 1.437
Z-value...................: 0.731202
P-value...................: 0.4646559
Hosmer and Lemeshow r^2...: 0.068172
Cox and Snell r^2.........: 0.069732
Nagelkerke's pseudo r^2...: 0.106682
Sample size of AE DB......: 2388
Sample size of model......: 319
Missing data %............: 86.64154
- processing Fat10Perc
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Gender + Hypertension.composite +
Stroke_history + IL6_pg_ug_2015_LN, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) Gendermale Hypertension.compositeyes Stroke_history IL6_pg_ug_2015_LN
0.9404 0.8368 0.8895 0.6832 0.2639
Degrees of Freedom: 318 Total (i.e. Null); 314 Residual
Null Deviance: 296.4
Residual Deviance: 276.5 AIC: 286.5
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + IL6_pg_ug_2015_LN, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.5998 0.3280 0.5023 0.6613 1.3541
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.659e+01 1.693e+03 0.010 0.99218
currentDF[, PROTEIN] -1.287e-02 1.496e-01 -0.086 0.93142
Age -1.270e-02 2.136e-02 -0.594 0.55226
Gendermale 9.800e-01 3.428e-01 2.859 0.00425 **
Hypertension.compositeyes 8.923e-01 4.375e-01 2.039 0.04140 *
DiabetesStatusDiabetes -1.737e-01 4.043e-01 -0.430 0.66748
SmokerCurrentyes -5.799e-02 3.613e-01 -0.161 0.87248
Med.Statin.LLDyes -1.196e-01 3.675e-01 -0.326 0.74475
Med.all.antiplateletyes 9.547e-02 5.545e-01 0.172 0.86331
GFR_MDRD -8.596e-03 9.983e-03 -0.861 0.38916
BMI 1.285e-02 4.392e-02 0.293 0.76981
CAD_history -2.536e-01 3.602e-01 -0.704 0.48136
Stroke_history 7.146e-01 3.673e-01 1.946 0.05170 .
Peripheral.interv -1.081e-01 3.816e-01 -0.283 0.77690
stenose50-70% -1.543e+01 1.693e+03 -0.009 0.99273
stenose70-90% -1.427e+01 1.693e+03 -0.008 0.99327
stenose90-99% -1.452e+01 1.693e+03 -0.009 0.99316
stenose100% (Occlusion) 5.480e-01 2.137e+03 0.000 0.99980
IL6_pg_ug_2015_LN 2.491e-01 1.126e-01 2.212 0.02697 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 296.40 on 318 degrees of freedom
Residual deviance: 269.53 on 300 degrees of freedom
AIC: 307.53
Number of Fisher Scoring iterations: 15
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_LN ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_LN
Trait/outcome.............: Fat10Perc
Effect size...............: -0.012874
Standard error............: 0.149596
Odds ratio (effect size)..: 0.987
Lower 95% CI..............: 0.736
Upper 95% CI..............: 1.324
Z-value...................: -0.086057
P-value...................: 0.9314209
Hosmer and Lemeshow r^2...: 0.090654
Cox and Snell r^2.........: 0.080781
Nagelkerke's pseudo r^2...: 0.133498
Sample size of AE DB......: 2388
Sample size of model......: 319
Missing data %............: 86.64154
- processing IPH
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Gender + Hypertension.composite +
GFR_MDRD, family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) Gendermale Hypertension.compositeyes GFR_MDRD
1.27860 0.64039 0.57993 -0.01649
Degrees of Freedom: 318 Total (i.e. Null); 315 Residual
Null Deviance: 365.7
Residual Deviance: 353.2 AIC: 361.2
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + IL6_pg_ug_2015_LN, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.0936 -1.1115 0.6435 0.8045 1.3204
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 13.326245 623.802229 0.021 0.9830
currentDF[, PROTEIN] -0.083005 0.127209 -0.653 0.5141
Age 0.013635 0.017553 0.777 0.4373
Gendermale 0.614092 0.293958 2.089 0.0367 *
Hypertension.compositeyes 0.675576 0.386942 1.746 0.0808 .
DiabetesStatusDiabetes -0.550357 0.335330 -1.641 0.1007
SmokerCurrentyes 0.062372 0.307216 0.203 0.8391
Med.Statin.LLDyes 0.002723 0.308712 0.009 0.9930
Med.all.antiplateletyes -0.783958 0.578683 -1.355 0.1755
GFR_MDRD -0.012688 0.008376 -1.515 0.1298
BMI 0.036855 0.037293 0.988 0.3230
CAD_history 0.021073 0.315139 0.067 0.9467
Stroke_history 0.106885 0.286648 0.373 0.7092
Peripheral.interv 0.269576 0.354570 0.760 0.4471
stenose50-70% -12.761963 623.798211 -0.020 0.9837
stenose70-90% -12.986066 623.797895 -0.021 0.9834
stenose90-99% -12.843961 623.797875 -0.021 0.9836
stenose100% (Occlusion) -13.670628 623.799581 -0.022 0.9825
IL6_pg_ug_2015_LN 0.095395 0.091250 1.045 0.2958
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 365.74 on 318 degrees of freedom
Residual deviance: 344.46 on 300 degrees of freedom
AIC: 382.46
Number of Fisher Scoring iterations: 13
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_LN ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_LN
Trait/outcome.............: IPH
Effect size...............: -0.083005
Standard error............: 0.127209
Odds ratio (effect size)..: 0.92
Lower 95% CI..............: 0.717
Upper 95% CI..............: 1.181
Z-value...................: -0.652513
P-value...................: 0.5140706
Hosmer and Lemeshow r^2...: 0.058163
Cox and Snell r^2.........: 0.06451
Nagelkerke's pseudo r^2...: 0.094554
Sample size of AE DB......: 2388
Sample size of model......: 319
Missing data %............: 86.64154
Analysis of MCP1_LN.
- processing CalcificationPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
DiabetesStatus + GFR_MDRD + Stroke_history + Peripheral.interv,
family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] DiabetesStatusDiabetes GFR_MDRD Stroke_history
2.75963 -0.23220 -0.69021 -0.01253 -0.50821
Peripheral.interv
-0.56921
Degrees of Freedom: 390 Total (i.e. Null); 385 Residual
Null Deviance: 529.8
Residual Deviance: 508.7 AIC: 520.7
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + IL6_pg_ug_2015_LN, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.9390 -1.2075 0.7881 1.0028 1.6679
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.615657 2.401219 0.256 0.79765
currentDF[, PROTEIN] -0.215684 0.137600 -1.567 0.11700
Age 0.015147 0.013814 1.097 0.27285
Gendermale -0.205593 0.243600 -0.844 0.39868
Hypertension.compositeyes 0.471313 0.322171 1.463 0.14349
DiabetesStatusDiabetes -0.720366 0.273492 -2.634 0.00844 **
SmokerCurrentyes 0.156046 0.236488 0.660 0.50935
Med.Statin.LLDyes -0.077618 0.251245 -0.309 0.75737
Med.all.antiplateletyes 0.276338 0.391583 0.706 0.48038
GFR_MDRD -0.010076 0.006240 -1.615 0.10635
BMI 0.003885 0.029690 0.131 0.89588
CAD_history -0.015261 0.250409 -0.061 0.95140
Stroke_history -0.555223 0.228526 -2.430 0.01512 *
Peripheral.interv -0.560383 0.285195 -1.965 0.04942 *
stenose50-70% 0.572791 1.638818 0.350 0.72670
stenose70-90% 0.573134 1.547009 0.370 0.71103
stenose90-99% 0.260900 1.540455 0.169 0.86551
stenose100% (Occlusion) 1.170352 2.033339 0.576 0.56490
IL6_pg_ug_2015_LN 0.033806 0.073081 0.463 0.64366
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 529.80 on 390 degrees of freedom
Residual deviance: 502.25 on 372 degrees of freedom
AIC: 540.25
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_LN ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_LN
Trait/outcome.............: CalcificationPlaque
Effect size...............: -0.215684
Standard error............: 0.1376
Odds ratio (effect size)..: 0.806
Lower 95% CI..............: 0.615
Upper 95% CI..............: 1.055
Z-value...................: -1.567475
P-value...................: 0.1170036
Hosmer and Lemeshow r^2...: 0.052
Cox and Snell r^2.........: 0.068034
Nagelkerke's pseudo r^2...: 0.091684
Sample size of AE DB......: 2388
Sample size of model......: 391
Missing data %............: 83.62647
- processing CollagenPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
DiabetesStatus + Med.all.antiplatelet + Peripheral.interv +
stenose, family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] DiabetesStatusDiabetes Med.all.antiplateletyes Peripheral.interv
19.836681 -0.605307 0.496906 0.945983 -0.497109
stenose50-70% stenose70-90% stenose90-99% stenose100% (Occlusion)
-0.362219 -16.187313 -16.664391 -0.008556
Degrees of Freedom: 390 Total (i.e. Null); 382 Residual
Null Deviance: 393.5
Residual Deviance: 360.5 AIC: 378.5
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + IL6_pg_ug_2015_LN, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.37247 0.00035 0.55119 0.69395 1.29272
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.976e+01 2.690e+03 0.007 0.99414
currentDF[, PROTEIN] -5.976e-01 1.839e-01 -3.250 0.00115 **
Age 9.003e-03 1.747e-02 0.515 0.60623
Gendermale -9.870e-02 3.054e-01 -0.323 0.74655
Hypertension.compositeyes 1.539e-01 3.957e-01 0.389 0.69738
DiabetesStatusDiabetes 5.913e-01 3.780e-01 1.564 0.11779
SmokerCurrentyes 3.815e-01 3.000e-01 1.272 0.20350
Med.Statin.LLDyes 6.889e-02 3.025e-01 0.228 0.81986
Med.all.antiplateletyes 9.329e-01 4.358e-01 2.141 0.03231 *
GFR_MDRD -1.867e-03 7.733e-03 -0.241 0.80920
BMI -3.382e-02 3.913e-02 -0.864 0.38744
CAD_history 4.504e-02 3.045e-01 0.148 0.88240
Stroke_history 3.011e-01 2.941e-01 1.024 0.30583
Peripheral.interv -5.120e-01 3.361e-01 -1.523 0.12774
stenose50-70% -2.787e-01 2.878e+03 0.000 0.99992
stenose70-90% -1.612e+01 2.690e+03 -0.006 0.99522
stenose90-99% -1.663e+01 2.690e+03 -0.006 0.99507
stenose100% (Occlusion) 1.404e-02 3.236e+03 0.000 1.00000
IL6_pg_ug_2015_LN -1.417e-02 9.018e-02 -0.157 0.87517
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 393.52 on 390 degrees of freedom
Residual deviance: 355.78 on 372 degrees of freedom
AIC: 393.78
Number of Fisher Scoring iterations: 16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_LN ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_LN
Trait/outcome.............: CollagenPlaque
Effect size...............: -0.597578
Standard error............: 0.183866
Odds ratio (effect size)..: 0.55
Lower 95% CI..............: 0.384
Upper 95% CI..............: 0.789
Z-value...................: -3.250077
P-value...................: 0.001153738
Hosmer and Lemeshow r^2...: 0.095909
Cox and Snell r^2.........: 0.092015
Nagelkerke's pseudo r^2...: 0.145023
Sample size of AE DB......: 2388
Sample size of model......: 391
Missing data %............: 83.62647
- processing Fat10Perc
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Gender + Hypertension.composite + BMI + Stroke_history +
IL6_pg_ug_2015_LN, family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Gendermale Hypertension.compositeyes BMI
-3.32554 0.60420 0.82335 0.68998 0.05451
Stroke_history IL6_pg_ug_2015_LN
0.64154 0.17450
Degrees of Freedom: 390 Total (i.e. Null); 384 Residual
Null Deviance: 367.5
Residual Deviance: 326.7 AIC: 340.7
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + IL6_pg_ug_2015_LN, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.6873 0.2956 0.4470 0.6240 1.7142
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 9.920e+00 9.783e+02 0.010 0.991909
currentDF[, PROTEIN] 6.236e-01 1.805e-01 3.455 0.000549 ***
Age 6.051e-04 1.827e-02 0.033 0.973581
Gendermale 8.943e-01 3.020e-01 2.961 0.003065 **
Hypertension.compositeyes 8.125e-01 4.021e-01 2.020 0.043352 *
DiabetesStatusDiabetes -2.452e-01 3.580e-01 -0.685 0.493320
SmokerCurrentyes 1.535e-02 3.156e-01 0.049 0.961216
Med.Statin.LLDyes -2.041e-01 3.500e-01 -0.583 0.559900
Med.all.antiplateletyes 4.440e-02 5.323e-01 0.083 0.933531
GFR_MDRD 1.616e-03 8.527e-03 0.190 0.849698
BMI 5.189e-02 3.986e-02 1.302 0.192964
CAD_history -2.668e-01 3.395e-01 -0.786 0.431944
Stroke_history 6.524e-01 3.304e-01 1.974 0.048329 *
Peripheral.interv 1.371e-01 3.784e-01 0.362 0.717064
stenose50-70% -1.464e+01 9.783e+02 -0.015 0.988061
stenose70-90% -1.308e+01 9.783e+02 -0.013 0.989335
stenose90-99% -1.351e+01 9.783e+02 -0.014 0.988977
stenose100% (Occlusion) -1.354e+01 9.783e+02 -0.014 0.988957
IL6_pg_ug_2015_LN 1.574e-01 1.003e-01 1.570 0.116511
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 367.47 on 390 degrees of freedom
Residual deviance: 318.31 on 372 degrees of freedom
AIC: 356.31
Number of Fisher Scoring iterations: 14
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_LN ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_LN
Trait/outcome.............: Fat10Perc
Effect size...............: 0.623593
Standard error............: 0.18047
Odds ratio (effect size)..: 1.866
Lower 95% CI..............: 1.31
Upper 95% CI..............: 2.657
Z-value...................: 3.455391
P-value...................: 0.0005494961
Hosmer and Lemeshow r^2...: 0.133787
Cox and Snell r^2.........: 0.118153
Nagelkerke's pseudo r^2...: 0.193914
Sample size of AE DB......: 2388
Sample size of model......: 391
Missing data %............: 83.62647
- processing IPH
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + Gender +
BMI, family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) Age Gendermale BMI
-2.47828 0.02961 0.56213 0.04578
Degrees of Freedom: 390 Total (i.e. Null); 387 Residual
Null Deviance: 442.5
Residual Deviance: 430.1 AIC: 438.1
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + IL6_pg_ug_2015_LN, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.0691 -0.8812 0.6388 0.7834 1.5392
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 10.956858 617.026269 0.018 0.9858
currentDF[, PROTEIN] 0.058760 0.152206 0.386 0.6995
Age 0.020541 0.015307 1.342 0.1796
Gendermale 0.587393 0.261581 2.246 0.0247 *
Hypertension.compositeyes 0.413722 0.344099 1.202 0.2292
DiabetesStatusDiabetes -0.444173 0.299202 -1.485 0.1377
SmokerCurrentyes -0.071966 0.266152 -0.270 0.7869
Med.Statin.LLDyes -0.080150 0.284447 -0.282 0.7781
Med.all.antiplateletyes -0.115336 0.453960 -0.254 0.7994
GFR_MDRD -0.006601 0.007114 -0.928 0.3535
BMI 0.047927 0.033475 1.432 0.1522
CAD_history 0.123168 0.294174 0.419 0.6754
Stroke_history 0.240489 0.261867 0.918 0.3584
Peripheral.interv 0.364679 0.343814 1.061 0.2888
stenose50-70% -12.703643 617.023149 -0.021 0.9836
stenose70-90% -12.917664 617.022854 -0.021 0.9833
stenose90-99% -12.667837 617.022837 -0.021 0.9836
stenose100% (Occlusion) -12.703899 617.024124 -0.021 0.9836
IL6_pg_ug_2015_LN 0.054674 0.082702 0.661 0.5086
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 442.47 on 390 degrees of freedom
Residual deviance: 419.44 on 372 degrees of freedom
AIC: 457.44
Number of Fisher Scoring iterations: 13
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_LN ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_LN
Trait/outcome.............: IPH
Effect size...............: 0.05876
Standard error............: 0.152206
Odds ratio (effect size)..: 1.061
Lower 95% CI..............: 0.787
Upper 95% CI..............: 1.429
Z-value...................: 0.386059
P-value...................: 0.699453
Hosmer and Lemeshow r^2...: 0.052051
Cox and Snell r^2.........: 0.057201
Nagelkerke's pseudo r^2...: 0.084431
Sample size of AE DB......: 2388
Sample size of model......: 391
Missing data %............: 83.62647
Analysis of IL6_pg_ug_2015_LN.
- processing CalcificationPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + SmokerCurrent + CAD_history + stenose, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Age SmokerCurrentyes CAD_history
-1.44653 -0.07084 0.01994 0.39936 0.25473
stenose50-70% stenose70-90% stenose90-99% stenose100% (Occlusion) stenose50-99%
-1.00356 -0.50547 -0.26062 0.82473 -13.93339
stenose70-99%
-1.52574
Degrees of Freedom: 996 Total (i.e. Null); 986 Residual
Null Deviance: 1381
Residual Deviance: 1349 AIC: 1371
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + IL6_pg_ug_2015_LN, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.6142 -1.1333 -0.7968 1.1560 1.6718
Coefficients: (1 not defined because of singularities)
Estimate Std. Error z value Pr(>|z|)
(Intercept) -1.129798 1.325225 -0.853 0.39392
currentDF[, PROTEIN] -0.069823 0.045164 -1.546 0.12211
Age 0.016974 0.008100 2.095 0.03613 *
Gendermale -0.133605 0.144147 -0.927 0.35400
Hypertension.compositeyes 0.232025 0.202513 1.146 0.25191
DiabetesStatusDiabetes -0.174736 0.159528 -1.095 0.27337
SmokerCurrentyes 0.414304 0.146715 2.824 0.00474 **
Med.Statin.LLDyes -0.164594 0.159158 -1.034 0.30106
Med.all.antiplateletyes -0.223848 0.217190 -1.031 0.30270
GFR_MDRD -0.001882 0.003492 -0.539 0.58999
BMI 0.012407 0.018096 0.686 0.49295
CAD_history 0.263345 0.149976 1.756 0.07910 .
Stroke_history -0.138179 0.140751 -0.982 0.32623
Peripheral.interv -0.183211 0.172892 -1.060 0.28929
stenose50-70% -0.937991 0.962569 -0.974 0.32983
stenose70-90% -0.484572 0.928921 -0.522 0.60191
stenose90-99% -0.235047 0.928443 -0.253 0.80014
stenose100% (Occlusion) 0.820707 1.245138 0.659 0.50981
stenose50-99% -14.005575 368.450051 -0.038 0.96968
stenose70-99% -1.450206 1.253664 -1.157 0.24736
IL6_pg_ug_2015_LN NA NA NA NA
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1381.3 on 996 degrees of freedom
Residual deviance: 1340.1 on 977 degrees of freedom
AIC: 1380.1
Number of Fisher Scoring iterations: 12
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_LN ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_pg_ug_2015_LN
Trait/outcome.............: CalcificationPlaque
Effect size...............: -0.069823
Standard error............: 0.045164
Odds ratio (effect size)..: 0.933
Lower 95% CI..............: 0.854
Upper 95% CI..............: 1.019
Z-value...................: -1.545965
P-value...................: 0.122113
Hosmer and Lemeshow r^2...: 0.029786
Cox and Snell r^2.........: 0.040427
Nagelkerke's pseudo r^2...: 0.053918
Sample size of AE DB......: 2388
Sample size of model......: 997
Missing data %............: 58.24958
- processing CollagenPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Hypertension.composite + SmokerCurrent + BMI + Stroke_history,
family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Hypertension.compositeyes SmokerCurrentyes BMI
-0.64730 -0.19549 0.33966 0.44766 0.03332
Stroke_history
0.25010
Degrees of Freedom: 999 Total (i.e. Null); 994 Residual
Null Deviance: 1017
Residual Deviance: 993.2 AIC: 1005
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + IL6_pg_ug_2015_LN, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.2738 0.4496 0.6096 0.7198 1.1160
Coefficients: (1 not defined because of singularities)
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.232e+01 6.478e+02 0.019 0.984826
currentDF[, PROTEIN] -1.901e-01 5.643e-02 -3.370 0.000753 ***
Age 8.224e-03 9.876e-03 0.833 0.405022
Gendermale -8.624e-02 1.781e-01 -0.484 0.628235
Hypertension.compositeyes 3.110e-01 2.330e-01 1.335 0.181998
DiabetesStatusDiabetes 2.110e-01 2.038e-01 1.035 0.300511
SmokerCurrentyes 4.928e-01 1.863e-01 2.645 0.008165 **
Med.Statin.LLDyes -1.679e-02 1.955e-01 -0.086 0.931556
Med.all.antiplateletyes 1.321e-01 2.616e-01 0.505 0.613723
GFR_MDRD 4.699e-03 4.301e-03 1.092 0.274655
BMI 3.343e-02 2.363e-02 1.415 0.157050
CAD_history 2.203e-01 1.882e-01 1.171 0.241696
Stroke_history 2.401e-01 1.765e-01 1.360 0.173700
Peripheral.interv -1.730e-02 2.154e-01 -0.080 0.935980
stenose50-70% -1.361e+01 6.478e+02 -0.021 0.983245
stenose70-90% -1.401e+01 6.478e+02 -0.022 0.982741
stenose90-99% -1.406e+01 6.478e+02 -0.022 0.982686
stenose100% (Occlusion) 5.521e-01 8.166e+02 0.001 0.999461
stenose50-99% 1.725e-02 1.208e+03 0.000 0.999989
stenose70-99% -1.372e+01 6.478e+02 -0.021 0.983109
IL6_pg_ug_2015_LN NA NA NA NA
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1017.22 on 999 degrees of freedom
Residual deviance: 980.22 on 980 degrees of freedom
AIC: 1020.2
Number of Fisher Scoring iterations: 14
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_LN ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_pg_ug_2015_LN
Trait/outcome.............: CollagenPlaque
Effect size...............: -0.190149
Standard error............: 0.056431
Odds ratio (effect size)..: 0.827
Lower 95% CI..............: 0.74
Upper 95% CI..............: 0.924
Z-value...................: -3.369571
P-value...................: 0.000752854
Hosmer and Lemeshow r^2...: 0.03637
Cox and Snell r^2.........: 0.036321
Nagelkerke's pseudo r^2...: 0.056893
Sample size of AE DB......: 2388
Sample size of model......: 1000
Missing data %............: 58.12395
- processing Fat10Perc
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Gender + Stroke_history + Peripheral.interv, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Gendermale Stroke_history Peripheral.interv
1.5088 0.3120 0.8361 0.3705 -0.6128
Degrees of Freedom: 999 Total (i.e. Null); 995 Residual
Null Deviance: 1165
Residual Deviance: 1078 AIC: 1088
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + IL6_pg_ug_2015_LN, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.2376 -1.0048 0.6046 0.8035 2.0522
Coefficients: (1 not defined because of singularities)
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.416e+01 3.908e+02 0.036 0.971101
currentDF[, PROTEIN] 3.150e-01 5.445e-02 5.785 7.25e-09 ***
Age 9.863e-03 9.331e-03 1.057 0.290516
Gendermale 8.540e-01 1.612e-01 5.299 1.16e-07 ***
Hypertension.compositeyes 6.925e-02 2.349e-01 0.295 0.768118
DiabetesStatusDiabetes -1.188e-01 1.840e-01 -0.646 0.518436
SmokerCurrentyes 9.721e-02 1.711e-01 0.568 0.569863
Med.Statin.LLDyes -1.762e-01 1.905e-01 -0.925 0.354945
Med.all.antiplateletyes 6.857e-02 2.500e-01 0.274 0.783889
GFR_MDRD -2.444e-04 4.099e-03 -0.060 0.952452
BMI 4.105e-03 2.044e-02 0.201 0.840876
CAD_history 8.625e-02 1.753e-01 0.492 0.622789
Stroke_history 3.758e-01 1.703e-01 2.207 0.027313 *
Peripheral.interv -6.179e-01 1.860e-01 -3.322 0.000895 ***
stenose50-70% -1.354e+01 3.908e+02 -0.035 0.972374
stenose70-90% -1.348e+01 3.908e+02 -0.035 0.972476
stenose90-99% -1.333e+01 3.908e+02 -0.034 0.972791
stenose100% (Occlusion) -1.432e+01 3.908e+02 -0.037 0.970764
stenose50-99% -1.499e+01 3.908e+02 -0.038 0.969397
stenose70-99% -1.463e+01 3.908e+02 -0.037 0.970143
IL6_pg_ug_2015_LN NA NA NA NA
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1164.5 on 999 degrees of freedom
Residual deviance: 1066.6 on 980 degrees of freedom
AIC: 1106.6
Number of Fisher Scoring iterations: 13
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_LN ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_pg_ug_2015_LN
Trait/outcome.............: Fat10Perc
Effect size...............: 0.314972
Standard error............: 0.054447
Odds ratio (effect size)..: 1.37
Lower 95% CI..............: 1.232
Upper 95% CI..............: 1.525
Z-value...................: 5.78494
P-value...................: 7.25384e-09
Hosmer and Lemeshow r^2...: 0.084067
Cox and Snell r^2.........: 0.093259
Nagelkerke's pseudo r^2...: 0.135564
Sample size of AE DB......: 2388
Sample size of model......: 1000
Missing data %............: 58.12395
- processing IPH
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Gender + Med.Statin.LLD +
BMI + CAD_history + Stroke_history + stenose, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) Gendermale Med.Statin.LLDyes BMI CAD_history
0.16028 0.59271 -0.25963 0.02923 0.30318
Stroke_history stenose50-70% stenose70-90% stenose90-99% stenose100% (Occlusion)
0.25028 -1.09743 -0.97086 -0.65159 -0.80087
stenose50-99% stenose70-99%
-15.27559 0.59126
Degrees of Freedom: 998 Total (i.e. Null); 987 Residual
Null Deviance: 1331
Residual Deviance: 1288 AIC: 1312
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + IL6_pg_ug_2015_LN, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.9361 -1.2697 0.8098 0.9793 1.4501
Coefficients: (1 not defined because of singularities)
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.257533 1.493884 0.172 0.8631
currentDF[, PROTEIN] 0.039778 0.046572 0.854 0.3930
Age 0.001667 0.008309 0.201 0.8410
Gendermale 0.639327 0.146514 4.364 1.28e-05 ***
Hypertension.compositeyes -0.107679 0.207227 -0.520 0.6033
DiabetesStatusDiabetes -0.123508 0.163543 -0.755 0.4501
SmokerCurrentyes 0.124348 0.151574 0.820 0.4120
Med.Statin.LLDyes -0.249362 0.167276 -1.491 0.1360
Med.all.antiplateletyes 0.162196 0.221065 0.734 0.4631
GFR_MDRD -0.004962 0.003614 -1.373 0.1698
BMI 0.033097 0.018698 1.770 0.0767 .
CAD_history 0.311877 0.157241 1.983 0.0473 *
Stroke_history 0.230077 0.146495 1.571 0.1163
Peripheral.interv 0.037229 0.178257 0.209 0.8346
stenose50-70% -1.012619 1.165333 -0.869 0.3849
stenose70-90% -0.905980 1.139839 -0.795 0.4267
stenose90-99% -0.591956 1.139857 -0.519 0.6035
stenose100% (Occlusion) -0.746506 1.357852 -0.550 0.5825
stenose50-99% -15.241325 376.943340 -0.040 0.9677
stenose70-99% 0.596010 1.581074 0.377 0.7062
IL6_pg_ug_2015_LN NA NA NA NA
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1331.0 on 998 degrees of freedom
Residual deviance: 1283.3 on 979 degrees of freedom
AIC: 1323.3
Number of Fisher Scoring iterations: 12
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_LN ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_pg_ug_2015_LN
Trait/outcome.............: IPH
Effect size...............: 0.039778
Standard error............: 0.046572
Odds ratio (effect size)..: 1.041
Lower 95% CI..............: 0.95
Upper 95% CI..............: 1.14
Z-value...................: 0.85412
P-value...................: 0.3930386
Hosmer and Lemeshow r^2...: 0.035859
Cox and Snell r^2.........: 0.046653
Nagelkerke's pseudo r^2...: 0.063374
Sample size of AE DB......: 2388
Sample size of model......: 999
Missing data %............: 58.16583
Analysis of IL6R_pg_ug_2015_LN.
- processing CalcificationPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + SmokerCurrent +
CAD_history + stenose + IL6_pg_ug_2015_LN, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) Age SmokerCurrentyes CAD_history stenose50-70%
-0.5484 0.0167 0.3493 0.2402 -1.6362
stenose70-90% stenose90-99% stenose100% (Occlusion) stenose50-99% stenose70-99%
-1.1930 -0.9512 0.1470 -14.6281 -2.1898
IL6_pg_ug_2015_LN
-0.0803
Degrees of Freedom: 963 Total (i.e. Null); 953 Residual
Null Deviance: 1336
Residual Deviance: 1307 AIC: 1329
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + IL6_pg_ug_2015_LN, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.6363 -1.1288 -0.8086 1.1628 1.6918
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.131467 1.509873 0.087 0.9306
currentDF[, PROTEIN] 0.015880 0.064650 0.246 0.8060
Age 0.012327 0.008316 1.482 0.1383
Gendermale -0.137060 0.147383 -0.930 0.3524
Hypertension.compositeyes 0.234963 0.204339 1.150 0.2502
DiabetesStatusDiabetes -0.225859 0.162459 -1.390 0.1645
SmokerCurrentyes 0.351674 0.149268 2.356 0.0185 *
Med.Statin.LLDyes -0.160052 0.163105 -0.981 0.3265
Med.all.antiplateletyes -0.279489 0.221207 -1.263 0.2064
GFR_MDRD -0.002737 0.003625 -0.755 0.4502
BMI 0.005206 0.018794 0.277 0.7818
CAD_history 0.256263 0.152852 1.677 0.0936 .
Stroke_history -0.127472 0.142976 -0.892 0.3726
Peripheral.interv -0.198088 0.177734 -1.115 0.2651
stenose50-70% -1.500411 1.195569 -1.255 0.2095
stenose70-90% -1.121396 1.168598 -0.960 0.3373
stenose90-99% -0.870897 1.168781 -0.745 0.4562
stenose100% (Occlusion) 0.178054 1.432143 0.124 0.9011
stenose50-99% -14.651114 369.668141 -0.040 0.9684
stenose70-99% -2.052663 1.439555 -1.426 0.1539
IL6_pg_ug_2015_LN -0.086194 0.049532 -1.740 0.0818 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1335.6 on 963 degrees of freedom
Residual deviance: 1297.1 on 943 degrees of freedom
AIC: 1339.1
Number of Fisher Scoring iterations: 12
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_LN ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6R_pg_ug_2015_LN
Trait/outcome.............: CalcificationPlaque
Effect size...............: 0.01588
Standard error............: 0.06465
Odds ratio (effect size)..: 1.016
Lower 95% CI..............: 0.895
Upper 95% CI..............: 1.153
Z-value...................: 0.245629
P-value...................: 0.8059697
Hosmer and Lemeshow r^2...: 0.028821
Cox and Snell r^2.........: 0.039144
Nagelkerke's pseudo r^2...: 0.052206
Sample size of AE DB......: 2388
Sample size of model......: 964
Missing data %............: 59.63149
- processing CollagenPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Hypertension.composite +
SmokerCurrent + CAD_history + Stroke_history + IL6_pg_ug_2015_LN,
family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) Hypertension.compositeyes SmokerCurrentyes CAD_history Stroke_history
0.1065 0.3455 0.4323 0.2732 0.2837
IL6_pg_ug_2015_LN
-0.2068
Degrees of Freedom: 966 Total (i.e. Null); 961 Residual
Null Deviance: 985.8
Residual Deviance: 960.7 AIC: 972.7
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + IL6_pg_ug_2015_LN, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.2145 0.4440 0.6086 0.7177 1.1783
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.259e+01 7.227e+02 0.017 0.986097
currentDF[, PROTEIN] 5.215e-02 8.232e-02 0.634 0.526381
Age 7.063e-03 1.015e-02 0.696 0.486455
Gendermale -3.182e-02 1.811e-01 -0.176 0.860579
Hypertension.compositeyes 3.185e-01 2.345e-01 1.358 0.174433
DiabetesStatusDiabetes 2.481e-01 2.084e-01 1.190 0.234003
SmokerCurrentyes 4.669e-01 1.891e-01 2.469 0.013536 *
Med.Statin.LLDyes -4.153e-03 2.003e-01 -0.021 0.983458
Med.all.antiplateletyes 1.838e-01 2.645e-01 0.695 0.487095
GFR_MDRD 3.662e-03 4.444e-03 0.824 0.409912
BMI 2.659e-02 2.439e-02 1.090 0.275597
CAD_history 2.855e-01 1.932e-01 1.477 0.139611
Stroke_history 2.739e-01 1.794e-01 1.527 0.126881
Peripheral.interv -6.486e-02 2.204e-01 -0.294 0.768565
stenose50-70% -1.368e+01 7.227e+02 -0.019 0.984895
stenose70-90% -1.407e+01 7.227e+02 -0.019 0.984466
stenose90-99% -1.409e+01 7.227e+02 -0.019 0.984450
stenose100% (Occlusion) 5.863e-01 8.745e+02 0.001 0.999465
stenose50-99% -6.587e-02 1.250e+03 0.000 0.999958
stenose70-99% -1.373e+01 7.227e+02 -0.019 0.984847
IL6_pg_ug_2015_LN -2.201e-01 6.190e-02 -3.555 0.000378 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 985.80 on 966 degrees of freedom
Residual deviance: 948.12 on 946 degrees of freedom
AIC: 990.12
Number of Fisher Scoring iterations: 14
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_LN ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6R_pg_ug_2015_LN
Trait/outcome.............: CollagenPlaque
Effect size...............: 0.052151
Standard error............: 0.082317
Odds ratio (effect size)..: 1.054
Lower 95% CI..............: 0.897
Upper 95% CI..............: 1.238
Z-value...................: 0.633541
P-value...................: 0.5263807
Hosmer and Lemeshow r^2...: 0.038216
Cox and Snell r^2.........: 0.03821
Nagelkerke's pseudo r^2...: 0.059778
Sample size of AE DB......: 2388
Sample size of model......: 967
Missing data %............: 59.50586
- processing Fat10Perc
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Gender + Stroke_history +
Peripheral.interv + IL6_pg_ug_2015_LN, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) Gendermale Stroke_history Peripheral.interv IL6_pg_ug_2015_LN
1.5720 0.7867 0.3729 -0.6177 0.3207
Degrees of Freedom: 966 Total (i.e. Null); 962 Residual
Null Deviance: 1120
Residual Deviance: 1038 AIC: 1048
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + IL6_pg_ug_2015_LN, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.2342 -1.0138 0.6048 0.7899 1.9678
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.395e+01 4.391e+02 0.032 0.97466
currentDF[, PROTEIN] -7.853e-02 7.347e-02 -1.069 0.28509
Age 1.118e-02 9.683e-03 1.154 0.24832
Gendermale 7.989e-01 1.656e-01 4.825 1.4e-06 ***
Hypertension.compositeyes 5.443e-02 2.395e-01 0.227 0.82023
DiabetesStatusDiabetes -7.246e-02 1.884e-01 -0.385 0.70045
SmokerCurrentyes 1.276e-01 1.752e-01 0.728 0.46643
Med.Statin.LLDyes -1.814e-01 1.955e-01 -0.928 0.35336
Med.all.antiplateletyes 7.743e-02 2.552e-01 0.303 0.76159
GFR_MDRD 5.217e-04 4.295e-03 0.121 0.90331
BMI -8.952e-04 2.143e-02 -0.042 0.96668
CAD_history 6.938e-02 1.792e-01 0.387 0.69860
Stroke_history 3.818e-01 1.736e-01 2.199 0.02785 *
Peripheral.interv -5.960e-01 1.914e-01 -3.114 0.00185 **
stenose50-70% -1.335e+01 4.391e+02 -0.030 0.97574
stenose70-90% -1.332e+01 4.391e+02 -0.030 0.97580
stenose90-99% -1.315e+01 4.391e+02 -0.030 0.97610
stenose100% (Occlusion) -1.417e+01 4.391e+02 -0.032 0.97425
stenose50-99% -1.479e+01 4.391e+02 -0.034 0.97314
stenose70-99% -1.456e+01 4.391e+02 -0.033 0.97355
IL6_pg_ug_2015_LN 3.472e-01 6.028e-02 5.760 8.4e-09 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1119.8 on 966 degrees of freedom
Residual deviance: 1026.6 on 946 degrees of freedom
AIC: 1068.6
Number of Fisher Scoring iterations: 13
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_LN ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6R_pg_ug_2015_LN
Trait/outcome.............: Fat10Perc
Effect size...............: -0.078534
Standard error............: 0.073468
Odds ratio (effect size)..: 0.924
Lower 95% CI..............: 0.8
Upper 95% CI..............: 1.068
Z-value...................: -1.068964
P-value...................: 0.285086
Hosmer and Lemeshow r^2...: 0.083245
Cox and Snell r^2.........: 0.091898
Nagelkerke's pseudo r^2...: 0.133984
Sample size of AE DB......: 2388
Sample size of model......: 967
Missing data %............: 59.50586
- processing IPH
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Gender + GFR_MDRD + CAD_history, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Gendermale GFR_MDRD CAD_history
0.579608 0.099350 0.609467 -0.005617 0.258415
Degrees of Freedom: 965 Total (i.e. Null); 961 Residual
Null Deviance: 1287
Residual Deviance: 1260 AIC: 1270
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + IL6_pg_ug_2015_LN, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.9401 -1.2758 0.8087 0.9790 1.4683
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.870087 1.542261 0.564 0.5726
currentDF[, PROTEIN] 0.075391 0.066105 1.140 0.2541
Age -0.002322 0.008571 -0.271 0.7864
Gendermale 0.652003 0.149841 4.351 1.35e-05 ***
Hypertension.compositeyes -0.110273 0.209219 -0.527 0.5981
DiabetesStatusDiabetes -0.162545 0.166002 -0.979 0.3275
SmokerCurrentyes 0.105910 0.154553 0.685 0.4932
Med.Statin.LLDyes -0.213342 0.171102 -1.247 0.2124
Med.all.antiplateletyes 0.100133 0.225696 0.444 0.6573
GFR_MDRD -0.006057 0.003763 -1.610 0.1075
BMI 0.021591 0.019375 1.114 0.2651
CAD_history 0.336940 0.160619 2.098 0.0359 *
Stroke_history 0.160102 0.148241 1.080 0.2801
Peripheral.interv 0.011627 0.183433 0.063 0.9495
stenose50-70% -0.707300 1.213509 -0.583 0.5600
stenose70-90% -0.719550 1.188270 -0.606 0.5448
stenose90-99% -0.430171 1.188886 -0.362 0.7175
stenose100% (Occlusion) -0.569412 1.397842 -0.407 0.6838
stenose50-99% -15.053372 375.980709 -0.040 0.9681
stenose70-99% 0.882110 1.614884 0.546 0.5849
IL6_pg_ug_2015_LN 0.019723 0.051089 0.386 0.6995
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1286.7 on 965 degrees of freedom
Residual deviance: 1241.9 on 945 degrees of freedom
AIC: 1283.9
Number of Fisher Scoring iterations: 12
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_LN ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6R_pg_ug_2015_LN
Trait/outcome.............: IPH
Effect size...............: 0.075391
Standard error............: 0.066105
Odds ratio (effect size)..: 1.078
Lower 95% CI..............: 0.947
Upper 95% CI..............: 1.227
Z-value...................: 1.140483
P-value...................: 0.2540851
Hosmer and Lemeshow r^2...: 0.034884
Cox and Snell r^2.........: 0.045403
Nagelkerke's pseudo r^2...: 0.061684
Sample size of AE DB......: 2388
Sample size of model......: 966
Missing data %............: 59.54774
Analysis of MCP1_pg_ug_2015_LN.
- processing CalcificationPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + SmokerCurrent + Med.Statin.LLD + CAD_history + IL6_pg_ug_2015_LN,
family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Age SmokerCurrentyes Med.Statin.LLDyes CAD_history
-1.68186 -0.46232 0.02094 0.43073 -0.24592 0.28851
IL6_pg_ug_2015_LN
0.12975
Degrees of Freedom: 995 Total (i.e. Null); 989 Residual
Null Deviance: 1380
Residual Deviance: 1300 AIC: 1314
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + IL6_pg_ug_2015_LN, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.0805 -1.0624 -0.6758 1.0971 2.0048
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -1.247022 1.352245 -0.922 0.3564
currentDF[, PROTEIN] -0.443863 0.061641 -7.201 5.99e-13 ***
Age 0.019515 0.008385 2.327 0.0199 *
Gendermale -0.114207 0.148426 -0.769 0.4416
Hypertension.compositeyes 0.175828 0.208273 0.844 0.3985
DiabetesStatusDiabetes -0.203647 0.164527 -1.238 0.2158
SmokerCurrentyes 0.424195 0.151463 2.801 0.0051 **
Med.Statin.LLDyes -0.237246 0.164315 -1.444 0.1488
Med.all.antiplateletyes -0.250764 0.222840 -1.125 0.2605
GFR_MDRD -0.001521 0.003610 -0.421 0.6735
BMI 0.011814 0.018663 0.633 0.5267
CAD_history 0.291490 0.154775 1.883 0.0597 .
Stroke_history -0.136104 0.144685 -0.941 0.3469
Peripheral.interv -0.129558 0.177907 -0.728 0.4665
stenose50-70% -0.808165 0.970477 -0.833 0.4050
stenose70-90% -0.351555 0.935233 -0.376 0.7070
stenose90-99% -0.169215 0.934686 -0.181 0.8563
stenose100% (Occlusion) 0.538300 1.257511 0.428 0.6686
stenose50-99% -13.583568 359.187307 -0.038 0.9698
stenose70-99% -1.070720 1.269341 -0.844 0.3989
IL6_pg_ug_2015_LN 0.116989 0.053110 2.203 0.0276 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1379.8 on 995 degrees of freedom
Residual deviance: 1283.1 on 975 degrees of freedom
AIC: 1325.1
Number of Fisher Scoring iterations: 12
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_LN ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_LN
Trait/outcome.............: CalcificationPlaque
Effect size...............: -0.443863
Standard error............: 0.061641
Odds ratio (effect size)..: 0.642
Lower 95% CI..............: 0.569
Upper 95% CI..............: 0.724
Z-value...................: -7.200765
P-value...................: 5.987587e-13
Hosmer and Lemeshow r^2...: 0.070094
Cox and Snell r^2.........: 0.092541
Nagelkerke's pseudo r^2...: 0.123426
Sample size of AE DB......: 2388
Sample size of model......: 996
Missing data %............: 58.29146
- processing CollagenPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
SmokerCurrent + BMI + Stroke_history + IL6_pg_ug_2015_LN,
family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] SmokerCurrentyes BMI Stroke_history IL6_pg_ug_2015_LN
-0.42580 -0.14037 0.41148 0.03668 0.25014 -0.14422
Degrees of Freedom: 998 Total (i.e. Null); 993 Residual
Null Deviance: 1017
Residual Deviance: 991.3 AIC: 1003
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + IL6_pg_ug_2015_LN, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.4197 0.4376 0.6130 0.7213 1.1331
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.226e+01 6.464e+02 0.019 0.98487
currentDF[, PROTEIN] -1.355e-01 7.212e-02 -1.879 0.06027 .
Age 8.672e-03 9.911e-03 0.875 0.38160
Gendermale -8.085e-02 1.786e-01 -0.453 0.65073
Hypertension.compositeyes 2.949e-01 2.331e-01 1.265 0.20584
DiabetesStatusDiabetes 2.090e-01 2.043e-01 1.023 0.30638
SmokerCurrentyes 4.927e-01 1.869e-01 2.637 0.00837 **
Med.Statin.LLDyes -3.188e-02 1.964e-01 -0.162 0.87101
Med.all.antiplateletyes 1.278e-01 2.616e-01 0.489 0.62514
GFR_MDRD 4.933e-03 4.319e-03 1.142 0.25337
BMI 3.417e-02 2.384e-02 1.433 0.15186
CAD_history 2.175e-01 1.887e-01 1.153 0.24897
Stroke_history 2.438e-01 1.767e-01 1.380 0.16769
Peripheral.interv -1.828e-03 2.160e-01 -0.008 0.99325
stenose50-70% -1.355e+01 6.464e+02 -0.021 0.98327
stenose70-90% -1.397e+01 6.464e+02 -0.022 0.98276
stenose90-99% -1.403e+01 6.464e+02 -0.022 0.98268
stenose100% (Occlusion) 4.510e-01 8.168e+02 0.001 0.99956
stenose50-99% 1.631e-01 1.205e+03 0.000 0.99989
stenose70-99% -1.358e+01 6.464e+02 -0.021 0.98324
IL6_pg_ug_2015_LN -1.360e-01 6.296e-02 -2.161 0.03070 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1016.8 on 998 degrees of freedom
Residual deviance: 976.4 on 978 degrees of freedom
AIC: 1018.4
Number of Fisher Scoring iterations: 14
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_LN ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_LN
Trait/outcome.............: CollagenPlaque
Effect size...............: -0.135492
Standard error............: 0.072117
Odds ratio (effect size)..: 0.873
Lower 95% CI..............: 0.758
Upper 95% CI..............: 1.006
Z-value...................: -1.878788
P-value...................: 0.06027341
Hosmer and Lemeshow r^2...: 0.039691
Cox and Snell r^2.........: 0.039592
Nagelkerke's pseudo r^2...: 0.061998
Sample size of AE DB......: 2388
Sample size of model......: 999
Missing data %............: 58.16583
- processing Fat10Perc
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Gender + Stroke_history +
Peripheral.interv + IL6_pg_ug_2015_LN, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) Gendermale Stroke_history Peripheral.interv IL6_pg_ug_2015_LN
1.5273 0.8331 0.3735 -0.6084 0.3181
Degrees of Freedom: 998 Total (i.e. Null); 994 Residual
Null Deviance: 1164
Residual Deviance: 1076 AIC: 1086
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + IL6_pg_ug_2015_LN, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.2589 -1.0101 0.6018 0.7920 2.0993
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.415e+01 3.921e+02 0.036 0.97120
currentDF[, PROTEIN] -7.151e-02 6.520e-02 -1.097 0.27273
Age 1.046e-02 9.349e-03 1.119 0.26300
Gendermale 8.575e-01 1.615e-01 5.310 1.10e-07 ***
Hypertension.compositeyes 5.600e-02 2.356e-01 0.238 0.81211
DiabetesStatusDiabetes -1.209e-01 1.842e-01 -0.657 0.51145
SmokerCurrentyes 9.488e-02 1.714e-01 0.554 0.57983
Med.Statin.LLDyes -1.720e-01 1.913e-01 -0.899 0.36850
Med.all.antiplateletyes 5.990e-02 2.504e-01 0.239 0.81092
GFR_MDRD -1.064e-04 4.107e-03 -0.026 0.97934
BMI 3.479e-03 2.047e-02 0.170 0.86504
CAD_history 7.993e-02 1.756e-01 0.455 0.64903
Stroke_history 3.777e-01 1.703e-01 2.218 0.02657 *
Peripheral.interv -6.043e-01 1.864e-01 -3.242 0.00119 **
stenose50-70% -1.350e+01 3.921e+02 -0.034 0.97253
stenose70-90% -1.346e+01 3.921e+02 -0.034 0.97262
stenose90-99% -1.332e+01 3.921e+02 -0.034 0.97290
stenose100% (Occlusion) -1.437e+01 3.921e+02 -0.037 0.97078
stenose50-99% -1.491e+01 3.921e+02 -0.038 0.96967
stenose70-99% -1.456e+01 3.921e+02 -0.037 0.97039
IL6_pg_ug_2015_LN 3.528e-01 6.258e-02 5.637 1.73e-08 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1163.9 on 998 degrees of freedom
Residual deviance: 1064.2 on 978 degrees of freedom
AIC: 1106.2
Number of Fisher Scoring iterations: 13
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_LN ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_LN
Trait/outcome.............: Fat10Perc
Effect size...............: -0.071508
Standard error............: 0.065197
Odds ratio (effect size)..: 0.931
Lower 95% CI..............: 0.819
Upper 95% CI..............: 1.058
Z-value...................: -1.096804
P-value...................: 0.2727272
Hosmer and Lemeshow r^2...: 0.085641
Cox and Snell r^2.........: 0.094961
Nagelkerke's pseudo r^2...: 0.138006
Sample size of AE DB......: 2388
Sample size of model......: 999
Missing data %............: 58.16583
- processing IPH
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Gender + Med.Statin.LLD + BMI + CAD_history + Stroke_history +
stenose + IL6_pg_ug_2015_LN, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Gendermale Med.Statin.LLDyes BMI
0.29150 -0.16175 0.61178 -0.30170 0.02949
CAD_history Stroke_history stenose50-70% stenose70-90% stenose90-99%
0.33164 0.24139 -1.04294 -0.93615 -0.63010
stenose100% (Occlusion) stenose50-99% stenose70-99% IL6_pg_ug_2015_LN
-0.94859 -15.14790 0.78602 0.10540
Degrees of Freedom: 997 Total (i.e. Null); 984 Residual
Null Deviance: 1329
Residual Deviance: 1277 AIC: 1305
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + IL6_pg_ug_2015_LN, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.9763 -1.2540 0.7873 0.9730 1.5792
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.329613 1.502995 0.219 0.82641
currentDF[, PROTEIN] -0.164322 0.059211 -2.775 0.00552 **
Age 0.001184 0.008365 0.142 0.88744
Gendermale 0.661474 0.147568 4.483 7.38e-06 ***
Hypertension.compositeyes -0.132227 0.208361 -0.635 0.52569
DiabetesStatusDiabetes -0.140503 0.164344 -0.855 0.39259
SmokerCurrentyes 0.110164 0.152548 0.722 0.47020
Med.Statin.LLDyes -0.296021 0.168954 -1.752 0.07976 .
Med.all.antiplateletyes 0.162553 0.222186 0.732 0.46441
GFR_MDRD -0.005252 0.003634 -1.445 0.14847
BMI 0.033085 0.018873 1.753 0.07959 .
CAD_history 0.331367 0.158390 2.092 0.03643 *
Stroke_history 0.235023 0.147134 1.597 0.11019
Peripheral.interv 0.050946 0.179307 0.284 0.77632
stenose50-70% -0.969413 1.172996 -0.826 0.40855
stenose70-90% -0.862961 1.147375 -0.752 0.45198
stenose90-99% -0.564964 1.147362 -0.492 0.62243
stenose100% (Occlusion) -0.861362 1.365522 -0.631 0.52818
stenose50-99% -15.111635 375.012524 -0.040 0.96786
stenose70-99% 0.760363 1.594802 0.477 0.63352
IL6_pg_ug_2015_LN 0.102235 0.053419 1.914 0.05564 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1329.1 on 997 degrees of freedom
Residual deviance: 1272.6 on 977 degrees of freedom
AIC: 1314.6
Number of Fisher Scoring iterations: 12
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_LN ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_LN
Trait/outcome.............: IPH
Effect size...............: -0.164322
Standard error............: 0.059211
Odds ratio (effect size)..: 0.848
Lower 95% CI..............: 0.756
Upper 95% CI..............: 0.953
Z-value...................: -2.775196
P-value...................: 0.005516838
Hosmer and Lemeshow r^2...: 0.04254
Cox and Snell r^2.........: 0.055078
Nagelkerke's pseudo r^2...: 0.074835
Sample size of AE DB......: 2388
Sample size of model......: 998
Missing data %............: 58.2077 cat("Edit the column names...\n")Edit the column names...colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
"Beta", "s.e.m.",
"OR", "low95CI", "up95CI",
"Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")
cat("Correct the variable types...\n")Correct the variable types...GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)
# Save the data
cat("Writing results to Excel-file...\n")Writing results to Excel-file...### Univariate
write.xlsx(GLM.results,
file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Multi.Protein.PlaquePhenotypes.MODEL5.xlsx"),
row.names = FALSE, col.names = TRUE, sheetName = "Bin.Multi.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")Removing intermediate files...rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)
Inverse-rank normalization
Here we use the inverse-rank normalized data - visually this is more normally distributed.
Analysis of continuous/quantitative plaque traits as a function of serum/plaque IL6(R)/MCP1 levels.
GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")Running linear regression...for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
PROTEIN = TRAITS.PROTEIN.RANK[protein]
cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
for (trait in 1:length(TRAITS.CON.RANK)) {
TRAIT = TRAITS.CON.RANK[trait]
cat(paste0("\n- processing ",TRAIT,"\n\n"))
currentDF <- as.data.frame(AEDB.CEA %>%
dplyr::select(., PROTEIN, TRAIT, COVARIATES_M5rank) %>%
filter(complete.cases(.))) %>%
filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
# for debug
# print(DT::datatable(currentDF))
# print(nrow(currentDF))
# print(str(currentDF))
### univariate
fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender +
Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose + IL6_pg_ug_2015_rank,
data = currentDF)
model_step <- stepAIC(fit, direction = "both", trace = FALSE)
print(model_step)
print(summary(fit))
GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
GLM.results.TEMP[1,] = GLM.CON(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
GLM.results = rbind(GLM.results, GLM.results.TEMP)
}
}
Analysis of IL6_rank.
- processing Macrophages_rankNote: Using an external vector in selections is ambiguous.
[34mℹ[39m Use `all_of(COVARIATES_M5rank)` instead of `COVARIATES_M5rank` to silence this message.
[34mℹ[39m See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
[90mThis message is displayed once per session.[39m
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Med.Statin.LLD +
Med.all.antiplatelet + CAD_history + IL6_pg_ug_2015_rank,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Med.Statin.LLDyes Med.all.antiplateletyes CAD_history
0.35989 -0.07726 -0.21850 -0.25457 0.16345
IL6_pg_ug_2015_rank
0.16591
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose +
IL6_pg_ug_2015_rank, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1.84744 -0.68617 0.08551 0.63084 2.76718
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.2031063 1.0305201 2.138 0.033217 *
currentDF[, TRAIT] -0.0742790 0.0467605 -1.589 0.113071
Age -0.0066882 0.0063518 -1.053 0.293082
Gendermale -0.1099818 0.1127155 -0.976 0.329860
Hypertension.compositeyes 0.0267508 0.1561093 0.171 0.864040
DiabetesStatusDiabetes -0.0280114 0.1276448 -0.219 0.826428
SmokerCurrentyes 0.0254452 0.1100166 0.231 0.817228
Med.Statin.LLDyes -0.2522453 0.1137454 -2.218 0.027218 *
Med.all.antiplateletyes -0.3217332 0.1793115 -1.794 0.073628 .
GFR_MDRD -0.0006063 0.0028743 -0.211 0.833055
BMI -0.0125403 0.0141299 -0.888 0.375415
CAD_history 0.1796808 0.1146154 1.568 0.117853
Stroke_history 0.0582772 0.1067541 0.546 0.585480
Peripheral.interv 0.0944270 0.1298333 0.727 0.467529
stenose50-70% -0.6811138 0.7287012 -0.935 0.350586
stenose70-90% -0.9512645 0.6846125 -1.389 0.165561
stenose90-99% -0.9277803 0.6818376 -1.361 0.174477
stenose100% (Occlusion) -1.3783689 0.8486138 -1.624 0.105216
IL6_pg_ug_2015_rank 0.1649700 0.0488477 3.377 0.000814 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9506 on 352 degrees of freedom
Multiple R-squared: 0.08119, Adjusted R-squared: 0.03421
F-statistic: 1.728 on 18 and 352 DF, p-value: 0.03306
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_rank ' with ' Macrophages_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_rank
Trait/outcome.............: Macrophages_rank
Effect size...............: -0.074279
Standard error............: 0.04676
Odds ratio (effect size)..: 0.928
Lower 95% CI..............: 0.847
Upper 95% CI..............: 1.018
T-value...................: -1.588501
P-value...................: 0.1130708
R^2.......................: 0.081194
Adjusted r^2..............: 0.03421
Sample size of AE DB......: 2388
Sample size of model......: 371
Missing data %............: 84.46399
- processing SMC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Med.Statin.LLD +
Med.all.antiplatelet + IL6_pg_ug_2015_rank, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Med.Statin.LLDyes Med.all.antiplateletyes IL6_pg_ug_2015_rank
0.39785 0.08688 -0.19540 -0.26346 0.17996
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose +
IL6_pg_ug_2015_rank, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1.92927 -0.67421 0.02866 0.64141 2.58659
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.0867567 1.0364337 2.013 0.044840 *
currentDF[, TRAIT] 0.0637693 0.0515246 1.238 0.216679
Age -0.0051687 0.0065107 -0.794 0.427807
Gendermale -0.1036623 0.1149227 -0.902 0.367669
Hypertension.compositeyes 0.0483875 0.1568018 0.309 0.757817
DiabetesStatusDiabetes -0.0160488 0.1278983 -0.125 0.900215
SmokerCurrentyes 0.0332253 0.1100576 0.302 0.762916
Med.Statin.LLDyes -0.2497548 0.1153095 -2.166 0.030991 *
Med.all.antiplateletyes -0.3157180 0.1799548 -1.754 0.080235 .
GFR_MDRD -0.0005197 0.0028850 -0.180 0.857160
BMI -0.0126116 0.0141800 -0.889 0.374405
CAD_history 0.1392717 0.1155741 1.205 0.229003
Stroke_history 0.0502429 0.1072670 0.468 0.639797
Peripheral.interv 0.0825096 0.1308781 0.630 0.528825
stenose50-70% -0.7117173 0.7319912 -0.972 0.331573
stenose70-90% -0.9703349 0.6867419 -1.413 0.158560
stenose90-99% -0.9317509 0.6838765 -1.362 0.173933
stenose100% (Occlusion) -1.3799752 0.8511497 -1.621 0.105855
IL6_pg_ug_2015_rank 0.1781599 0.0498129 3.577 0.000397 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9533 on 349 degrees of freedom
Multiple R-squared: 0.07877, Adjusted R-squared: 0.03126
F-statistic: 1.658 on 18 and 349 DF, p-value: 0.04502
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_rank ' with ' SMC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_rank
Trait/outcome.............: SMC_rank
Effect size...............: 0.063769
Standard error............: 0.051525
Odds ratio (effect size)..: 1.066
Lower 95% CI..............: 0.963
Upper 95% CI..............: 1.179
T-value...................: 1.237647
P-value...................: 0.2166789
R^2.......................: 0.078772
Adjusted r^2..............: 0.031259
Sample size of AE DB......: 2388
Sample size of model......: 368
Missing data %............: 84.58961
- processing VesselDensity_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Med.Statin.LLD + Med.all.antiplatelet +
IL6_pg_ug_2015_rank, data = currentDF)
Coefficients:
(Intercept) Med.Statin.LLDyes Med.all.antiplateletyes IL6_pg_ug_2015_rank
0.3946 -0.1937 -0.2655 0.1654
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose +
IL6_pg_ug_2015_rank, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1.90308 -0.67707 0.07394 0.63223 2.76744
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.150e+00 1.045e+00 2.058 0.040363 *
currentDF[, TRAIT] -6.949e-02 6.396e-02 -1.087 0.277974
Age -6.905e-03 6.442e-03 -1.072 0.284480
Gendermale -1.054e-01 1.141e-01 -0.923 0.356568
Hypertension.compositeyes 8.635e-02 1.604e-01 0.538 0.590637
DiabetesStatusDiabetes -1.333e-02 1.308e-01 -0.102 0.918894
SmokerCurrentyes 4.174e-02 1.116e-01 0.374 0.708632
Med.Statin.LLDyes -2.492e-01 1.155e-01 -2.158 0.031634 *
Med.all.antiplateletyes -3.307e-01 1.808e-01 -1.829 0.068294 .
GFR_MDRD 1.827e-05 2.932e-03 0.006 0.995032
BMI -1.267e-02 1.431e-02 -0.885 0.376633
CAD_history 1.581e-01 1.160e-01 1.363 0.173663
Stroke_history 9.170e-02 1.091e-01 0.840 0.401297
Peripheral.interv 9.252e-02 1.327e-01 0.697 0.486219
stenose50-70% -6.830e-01 7.339e-01 -0.931 0.352696
stenose70-90% -9.892e-01 6.899e-01 -1.434 0.152541
stenose90-99% -9.555e-01 6.868e-01 -1.391 0.165052
stenose100% (Occlusion) -1.414e+00 8.559e-01 -1.652 0.099411 .
IL6_pg_ug_2015_rank 1.670e-01 4.948e-02 3.374 0.000824 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9564 on 344 degrees of freedom
Multiple R-squared: 0.07774, Adjusted R-squared: 0.02949
F-statistic: 1.611 on 18 and 344 DF, p-value: 0.0551
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_rank ' with ' VesselDensity_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_rank
Trait/outcome.............: VesselDensity_rank
Effect size...............: -0.069495
Standard error............: 0.063956
Odds ratio (effect size)..: 0.933
Lower 95% CI..............: 0.823
Upper 95% CI..............: 1.057
T-value...................: -1.086601
P-value...................: 0.2779738
R^2.......................: 0.077744
Adjusted r^2..............: 0.029487
Sample size of AE DB......: 2388
Sample size of model......: 363
Missing data %............: 84.799
Analysis of MCP1_rank.
- processing Macrophages_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + Med.Statin.LLD +
Med.all.antiplatelet + CAD_history + Peripheral.interv +
IL6_pg_ug_2015_rank, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age Gendermale Hypertension.compositeyes
0.455323 0.098950 -0.007998 0.239787 -0.212282
DiabetesStatusDiabetes Med.Statin.LLDyes Med.all.antiplateletyes CAD_history Peripheral.interv
-0.274062 -0.208990 0.383777 0.199395 -0.177163
IL6_pg_ug_2015_rank
0.281379
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose +
IL6_pg_ug_2015_rank, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.66159 -0.65202 0.01668 0.65824 2.64720
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.210790 0.981220 1.234 0.2180
currentDF[, TRAIT] 0.093391 0.044733 2.088 0.0375 *
Age -0.010588 0.006050 -1.750 0.0809 .
Gendermale 0.242507 0.105839 2.291 0.0225 *
Hypertension.compositeyes -0.219585 0.144592 -1.519 0.1297
DiabetesStatusDiabetes -0.266244 0.120184 -2.215 0.0273 *
SmokerCurrentyes -0.047201 0.104990 -0.450 0.6533
Med.Statin.LLDyes -0.230203 0.110357 -2.086 0.0377 *
Med.all.antiplateletyes 0.326921 0.167864 1.948 0.0522 .
GFR_MDRD -0.002338 0.002739 -0.853 0.3939
BMI -0.008989 0.013129 -0.685 0.4940
CAD_history 0.195402 0.110685 1.765 0.0783 .
Stroke_history 0.092587 0.101208 0.915 0.3609
Peripheral.interv -0.190373 0.127441 -1.494 0.1361
stenose50-70% -0.081469 0.715759 -0.114 0.9094
stenose70-90% -0.095273 0.672675 -0.142 0.8874
stenose90-99% -0.124982 0.669862 -0.187 0.8521
stenose100% (Occlusion) -0.947978 0.832048 -1.139 0.2553
IL6_pg_ug_2015_rank 0.276120 0.045899 6.016 4.25e-09 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9349 on 376 degrees of freedom
Multiple R-squared: 0.1664, Adjusted R-squared: 0.1265
F-statistic: 4.171 on 18 and 376 DF, p-value: 4.738e-08
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' Macrophages_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: Macrophages_rank
Effect size...............: 0.093391
Standard error............: 0.044733
Odds ratio (effect size)..: 1.098
Lower 95% CI..............: 1.006
Upper 95% CI..............: 1.198
T-value...................: 2.087753
P-value...................: 0.0374927
R^2.......................: 0.16644
Adjusted r^2..............: 0.126535
Sample size of AE DB......: 2388
Sample size of model......: 395
Missing data %............: 83.45896
- processing SMC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + Med.Statin.LLD +
Med.all.antiplatelet + CAD_history + IL6_pg_ug_2015_rank,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age Gendermale Hypertension.compositeyes
0.77356 -0.13097 -0.01268 0.22471 -0.21870
DiabetesStatusDiabetes Med.Statin.LLDyes Med.all.antiplateletyes CAD_history IL6_pg_ug_2015_rank
-0.28091 -0.20124 0.37211 0.21861 0.24395
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose +
IL6_pg_ug_2015_rank, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.55335 -0.65341 -0.03949 0.65521 2.69818
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.554051 0.977225 1.590 0.1126
currentDF[, TRAIT] -0.130423 0.048350 -2.697 0.0073 **
Age -0.015344 0.006137 -2.500 0.0128 *
Gendermale 0.213022 0.106539 1.999 0.0463 *
Hypertension.compositeyes -0.231524 0.143610 -1.612 0.1078
DiabetesStatusDiabetes -0.267855 0.119386 -2.244 0.0254 *
SmokerCurrentyes -0.062128 0.103707 -0.599 0.5495
Med.Statin.LLDyes -0.215775 0.110095 -1.960 0.0508 .
Med.all.antiplateletyes 0.306341 0.166546 1.839 0.0667 .
GFR_MDRD -0.002438 0.002715 -0.898 0.3696
BMI -0.008989 0.013030 -0.690 0.4907
CAD_history 0.238812 0.110205 2.167 0.0309 *
Stroke_history 0.101300 0.100583 1.007 0.3145
Peripheral.interv -0.184149 0.127330 -1.446 0.1489
stenose50-70% -0.019812 0.710604 -0.028 0.9778
stenose70-90% -0.064875 0.667296 -0.097 0.9226
stenose90-99% -0.098145 0.664491 -0.148 0.8827
stenose100% (Occlusion) -0.945321 0.825343 -1.145 0.2528
IL6_pg_ug_2015_rank 0.241475 0.046528 5.190 3.46e-07 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9273 on 373 degrees of freedom
Multiple R-squared: 0.1722, Adjusted R-squared: 0.1323
F-statistic: 4.311 on 18 and 373 DF, p-value: 2.081e-08
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' SMC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: SMC_rank
Effect size...............: -0.130423
Standard error............: 0.04835
Odds ratio (effect size)..: 0.878
Lower 95% CI..............: 0.798
Upper 95% CI..............: 0.965
T-value...................: -2.697469
P-value...................: 0.007304015
R^2.......................: 0.172223
Adjusted r^2..............: 0.132277
Sample size of AE DB......: 2388
Sample size of model......: 392
Missing data %............: 83.58459
- processing VesselDensity_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + DiabetesStatus + Med.Statin.LLD + Med.all.antiplatelet +
CAD_history + IL6_pg_ug_2015_rank, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age Gendermale DiabetesStatusDiabetes
0.347892 -0.130851 -0.009673 0.291255 -0.237930
Med.Statin.LLDyes Med.all.antiplateletyes CAD_history IL6_pg_ug_2015_rank
-0.211412 0.394747 0.204490 0.281865
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose +
IL6_pg_ug_2015_rank, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.71687 -0.67661 -0.01886 0.67777 2.71172
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.227389 0.986479 1.244 0.21422
currentDF[, TRAIT] -0.139249 0.060068 -2.318 0.02099 *
Age -0.011612 0.006075 -1.911 0.05672 .
Gendermale 0.283848 0.106206 2.673 0.00786 **
Hypertension.compositeyes -0.190942 0.148027 -1.290 0.19789
DiabetesStatusDiabetes -0.217584 0.121942 -1.784 0.07520 .
SmokerCurrentyes -0.072471 0.105407 -0.688 0.49218
Med.Statin.LLDyes -0.222020 0.111033 -2.000 0.04628 *
Med.all.antiplateletyes 0.337245 0.167708 2.011 0.04507 *
GFR_MDRD -0.002070 0.002782 -0.744 0.45743
BMI -0.008220 0.013248 -0.620 0.53534
CAD_history 0.229973 0.111607 2.061 0.04005 *
Stroke_history 0.113074 0.102710 1.101 0.27166
Peripheral.interv -0.131189 0.129629 -1.012 0.31219
stenose50-70% -0.164915 0.714212 -0.231 0.81752
stenose70-90% -0.127877 0.671601 -0.190 0.84910
stenose90-99% -0.159676 0.668379 -0.239 0.81132
stenose100% (Occlusion) -1.011506 0.831005 -1.217 0.22431
IL6_pg_ug_2015_rank 0.276904 0.045999 6.020 4.24e-09 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9319 on 367 degrees of freedom
Multiple R-squared: 0.1718, Adjusted R-squared: 0.1312
F-statistic: 4.229 on 18 and 367 DF, p-value: 3.499e-08
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' VesselDensity_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: VesselDensity_rank
Effect size...............: -0.139249
Standard error............: 0.060068
Odds ratio (effect size)..: 0.87
Lower 95% CI..............: 0.773
Upper 95% CI..............: 0.979
T-value...................: -2.318182
P-value...................: 0.02098847
R^2.......................: 0.171797
Adjusted r^2..............: 0.131177
Sample size of AE DB......: 2388
Sample size of model......: 386
Missing data %............: 83.83585
Analysis of IL6_pg_ug_2015_rank.
- processing Macrophages_rankattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsense
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + DiabetesStatus + SmokerCurrent + Med.Statin.LLD +
GFR_MDRD + CAD_history + Stroke_history + Peripheral.interv +
stenose + IL6_pg_ug_2015_rank + Hypertension.composite, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age Gendermale DiabetesStatusDiabetes
2.247e-17 1.038e-17 -4.087e-19 9.218e-19 -3.176e-18
SmokerCurrentyes Med.Statin.LLDyes GFR_MDRD CAD_history Stroke_history
8.553e-18 -7.241e-18 -2.651e-19 -1.348e-17 2.148e-17
Peripheral.interv stenose50-70% stenose70-90% stenose90-99% stenose100% (Occlusion)
-1.680e-17 -9.075e-19 2.774e-17 2.150e-17 6.606e-17
stenose50-99% stenose70-99% IL6_pg_ug_2015_rank Hypertension.compositeyes
1.901e-18 -1.685e-17 1.000e+00 6.901e-18 essentially perfect fit: summary may be unreliable
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose +
IL6_pg_ug_2015_rank, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.048e-16 -2.335e-17 -1.240e-18 2.056e-17 1.998e-15
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.465e-16 5.066e-17 -2.891e+00 0.00393 **
currentDF[, TRAIT] -2.077e-17 2.546e-18 -8.157e+00 1.05e-15 ***
Age 8.697e-19 3.119e-19 2.788e+00 0.00540 **
Gendermale -2.298e-18 5.575e-18 -4.120e-01 0.68023
Hypertension.compositeyes 2.249e-17 7.759e-18 2.898e+00 0.00383 **
DiabetesStatusDiabetes 2.213e-18 6.128e-18 3.610e-01 0.71813
SmokerCurrentyes -1.421e-17 5.628e-18 -2.525e+00 0.01172 *
Med.Statin.LLDyes 1.420e-17 6.136e-18 2.314e+00 0.02089 *
Med.all.antiplateletyes -1.538e-18 8.327e-18 -1.850e-01 0.85347
GFR_MDRD 5.806e-19 1.339e-19 4.335e+00 1.61e-05 ***
BMI 1.866e-18 6.970e-19 2.677e+00 0.00754 **
CAD_history 2.443e-17 5.770e-18 4.233e+00 2.52e-05 ***
Stroke_history -4.160e-17 5.419e-18 -7.677e+00 3.94e-14 ***
Peripheral.interv 3.323e-17 6.658e-18 4.991e+00 7.12e-07 ***
stenose50-70% 2.052e-17 3.667e-17 5.600e-01 0.57593
stenose70-90% -3.799e-17 3.547e-17 -1.071e+00 0.28441
stenose90-99% -2.632e-17 3.545e-17 -7.420e-01 0.45796
stenose100% (Occlusion) -8.577e-17 4.509e-17 -1.902e+00 0.05742 .
stenose50-99% -3.117e-18 6.585e-17 -4.700e-02 0.96226
stenose70-99% 3.375e-17 4.777e-17 7.070e-01 0.47998
IL6_pg_ug_2015_rank 1.000e+00 2.555e-18 3.913e+17 < 2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 7.856e-17 on 975 degrees of freedom
Multiple R-squared: 1, Adjusted R-squared: 1
F-statistic: 7.984e+33 on 20 and 975 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_rank ' with ' Macrophages_rank ' .essentially perfect fit: summary may be unreliableCollecting data.essentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableWe have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_pg_ug_2015_rank
Trait/outcome.............: Macrophages_rank
Effect size...............: 0
Standard error............: 0
Odds ratio (effect size)..: 1
Lower 95% CI..............: 1
Upper 95% CI..............: 1
T-value...................: -8.156576
P-value...................: 1.054821e-15
R^2.......................: 1
Adjusted r^2..............: 1
Sample size of AE DB......: 2388
Sample size of model......: 996
Missing data %............: 58.29146
- processing SMC_rankattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsense
Call:
lm(formula = currentDF[, PROTEIN] ~ Age + Gender + Hypertension.composite +
SmokerCurrent + Med.Statin.LLD + GFR_MDRD + BMI + Stroke_history +
Peripheral.interv + stenose + IL6_pg_ug_2015_rank + currentDF[,
TRAIT], data = currentDF)
Coefficients:
(Intercept) Age Gendermale Hypertension.compositeyes SmokerCurrentyes
-1.572e-16 1.286e-18 -1.660e-18 2.490e-17 -1.283e-17
Med.Statin.LLDyes GFR_MDRD BMI Stroke_history Peripheral.interv
1.490e-17 5.157e-19 1.938e-18 -4.570e-17 4.006e-17
stenose50-70% stenose70-90% stenose90-99% stenose100% (Occlusion) stenose50-99%
1.437e-17 -5.063e-17 -3.897e-17 -1.201e-16 4.907e-18
stenose70-99% IL6_pg_ug_2015_rank currentDF[, TRAIT]
4.537e-17 1.000e+00 2.219e-18 essentially perfect fit: summary may be unreliable
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose +
IL6_pg_ug_2015_rank, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.584e-16 -3.610e-17 -7.100e-18 2.310e-17 6.025e-15
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.894e-16 1.305e-16 1.451e+00 0.14703
currentDF[, TRAIT] -3.614e-17 6.889e-18 -5.246e+00 1.91e-07 ***
Age -1.834e-18 8.128e-19 -2.257e+00 0.02426 *
Gendermale -7.294e-18 1.454e-17 -5.020e-01 0.61596
Hypertension.compositeyes -1.927e-17 1.997e-17 -9.650e-01 0.33487
DiabetesStatusDiabetes -1.416e-18 1.577e-17 -9.000e-02 0.92851
SmokerCurrentyes 1.331e-17 1.450e-17 9.180e-01 0.35863
Med.Statin.LLDyes -1.450e-17 1.582e-17 -9.160e-01 0.35967
Med.all.antiplateletyes -4.570e-18 2.143e-17 -2.130e-01 0.83118
GFR_MDRD -4.874e-19 3.451e-19 -1.412e+00 0.15818
BMI -1.694e-18 1.796e-18 -9.430e-01 0.34589
CAD_history -2.023e-17 1.486e-17 -1.361e+00 0.17378
Stroke_history 4.370e-17 1.394e-17 3.134e+00 0.00177 **
Peripheral.interv -3.586e-17 1.722e-17 -2.082e+00 0.03761 *
stenose50-70% -8.171e-18 9.437e-17 -8.700e-02 0.93102
stenose70-90% 5.711e-17 9.130e-17 6.260e-01 0.53177
stenose90-99% 4.816e-17 9.126e-17 5.280e-01 0.59780
stenose100% (Occlusion) 9.823e-17 1.160e-16 8.470e-01 0.39743
stenose50-99% 3.366e-17 1.696e-16 1.980e-01 0.84270
stenose70-99% -1.688e-17 1.229e-16 -1.370e-01 0.89078
IL6_pg_ug_2015_rank 1.000e+00 6.640e-18 1.506e+17 < 2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 2.021e-16 on 971 degrees of freedom
Multiple R-squared: 1, Adjusted R-squared: 1
F-statistic: 1.202e+33 on 20 and 971 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_rank ' with ' SMC_rank ' .essentially perfect fit: summary may be unreliableCollecting data.essentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableWe have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_pg_ug_2015_rank
Trait/outcome.............: SMC_rank
Effect size...............: 0
Standard error............: 0
Odds ratio (effect size)..: 1
Lower 95% CI..............: 1
Upper 95% CI..............: 1
T-value...................: -5.246188
P-value...................: 1.90645e-07
R^2.......................: 1
Adjusted r^2..............: 1
Sample size of AE DB......: 2388
Sample size of model......: 992
Missing data %............: 58.45896
- processing VesselDensity_rankattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsense
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + DiabetesStatus + SmokerCurrent + Med.Statin.LLD +
Med.all.antiplatelet + GFR_MDRD + BMI + CAD_history + Peripheral.interv +
stenose + IL6_pg_ug_2015_rank, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age Gendermale DiabetesStatusDiabetes
-1.378e-16 1.402e-17 7.842e-19 4.466e-20 1.115e-17
SmokerCurrentyes Med.Statin.LLDyes Med.all.antiplateletyes GFR_MDRD BMI
-1.803e-17 1.125e-17 3.124e-18 7.339e-19 1.730e-18
CAD_history Peripheral.interv stenose50-70% stenose70-90% stenose90-99%
2.810e-17 4.228e-17 1.752e-17 -4.549e-17 -3.570e-17
stenose100% (Occlusion) stenose50-99% stenose70-99% IL6_pg_ug_2015_rank
-1.115e-16 -1.037e-17 7.471e-17 1.000e+00 essentially perfect fit: summary may be unreliable
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose +
IL6_pg_ug_2015_rank, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-6.332e-15 -2.720e-17 6.100e-18 3.790e-17 3.518e-16
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.550e-16 1.437e-16 -1.079e+00 0.28107
currentDF[, TRAIT] 1.511e-17 7.444e-18 2.030e+00 0.04269 *
Age 9.594e-19 8.973e-19 1.069e+00 0.28525
Gendermale -8.395e-19 1.605e-17 -5.200e-02 0.95831
Hypertension.compositeyes 2.367e-17 2.235e-17 1.059e+00 0.28998
DiabetesStatusDiabetes 1.146e-17 1.806e-17 6.350e-01 0.52580
SmokerCurrentyes -1.388e-17 1.631e-17 -8.510e-01 0.39497
Med.Statin.LLDyes 1.210e-17 1.755e-17 6.900e-01 0.49068
Med.all.antiplateletyes 4.826e-18 2.430e-17 1.990e-01 0.84265
GFR_MDRD 7.155e-19 3.899e-19 1.835e+00 0.06682 .
BMI 1.311e-18 1.998e-18 6.560e-01 0.51187
CAD_history 2.138e-17 1.674e-17 1.277e+00 0.20194
Stroke_history -4.574e-17 1.566e-17 -2.921e+00 0.00358 **
Peripheral.interv 3.700e-17 1.960e-17 1.888e+00 0.05937 .
stenose50-70% 3.483e-17 1.030e-16 3.380e-01 0.73526
stenose70-90% -3.343e-17 9.918e-17 -3.370e-01 0.73619
stenose90-99% -2.482e-17 9.905e-17 -2.510e-01 0.80223
stenose100% (Occlusion) -7.879e-17 1.260e-16 -6.250e-01 0.53183
stenose50-99% -1.041e-17 1.838e-16 -5.700e-02 0.95482
stenose70-99% 7.479e-17 1.477e-16 5.060e-01 0.61273
IL6_pg_ug_2015_rank 1.000e+00 7.333e-18 1.364e+17 < 2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 2.192e-16 on 909 degrees of freedom
Multiple R-squared: 1, Adjusted R-squared: 1
F-statistic: 9.677e+32 on 20 and 909 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_rank ' with ' VesselDensity_rank ' .essentially perfect fit: summary may be unreliableCollecting data.essentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableWe have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_pg_ug_2015_rank
Trait/outcome.............: VesselDensity_rank
Effect size...............: 0
Standard error............: 0
Odds ratio (effect size)..: 1
Lower 95% CI..............: 1
Upper 95% CI..............: 1
T-value...................: 2.029581
P-value...................: 0.04268999
R^2.......................: 1
Adjusted r^2..............: 1
Sample size of AE DB......: 2388
Sample size of model......: 930
Missing data %............: 61.05528
Analysis of IL6R_pg_ug_2015_rank.
- processing Macrophages_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Med.Statin.LLD + GFR_MDRD + Peripheral.interv + stenose +
IL6_pg_ug_2015_rank, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age Med.Statin.LLDyes GFR_MDRD
0.150100 0.101725 -0.008416 -0.342529 -0.002550
Peripheral.interv stenose50-70% stenose70-90% stenose90-99% stenose100% (Occlusion)
0.257582 0.589170 0.828423 0.959326 0.531597
stenose50-99% stenose70-99% IL6_pg_ug_2015_rank
0.613400 0.005560 0.347977
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose +
IL6_pg_ug_2015_rank, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.44459 -0.60814 -0.00894 0.57895 2.91573
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.062717 0.620798 0.101 0.919551
currentDF[, TRAIT] 0.105434 0.029748 3.544 0.000413 ***
Age -0.007985 0.003669 -2.176 0.029799 *
Gendermale -0.078319 0.065425 -1.197 0.231575
Hypertension.compositeyes 0.113763 0.089867 1.266 0.205858
DiabetesStatusDiabetes -0.066427 0.071428 -0.930 0.352613
SmokerCurrentyes 0.027958 0.065807 0.425 0.671041
Med.Statin.LLDyes -0.342026 0.071458 -4.786 1.97e-06 ***
Med.all.antiplateletyes 0.057483 0.097149 0.592 0.554192
GFR_MDRD -0.002406 0.001591 -1.512 0.130914
BMI -0.002148 0.008305 -0.259 0.795963
CAD_history -0.019022 0.067382 -0.282 0.777780
Stroke_history 0.001197 0.062994 0.019 0.984839
Peripheral.interv 0.255591 0.078167 3.270 0.001115 **
stenose50-70% 0.617712 0.467816 1.320 0.187017
stenose70-90% 0.850410 0.454842 1.870 0.061838 .
stenose90-99% 0.974101 0.454624 2.143 0.032396 *
stenose100% (Occlusion) 0.569253 0.555373 1.025 0.305630
stenose50-99% 0.624106 0.781627 0.798 0.424799
stenose70-99% 0.028999 0.584347 0.050 0.960430
IL6_pg_ug_2015_rank 0.347123 0.029674 11.698 < 2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9007 on 943 degrees of freedom
Multiple R-squared: 0.1997, Adjusted R-squared: 0.1827
F-statistic: 11.76 on 20 and 943 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_rank ' with ' Macrophages_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6R_pg_ug_2015_rank
Trait/outcome.............: Macrophages_rank
Effect size...............: 0.105434
Standard error............: 0.029748
Odds ratio (effect size)..: 1.111
Lower 95% CI..............: 1.048
Upper 95% CI..............: 1.178
T-value...................: 3.54422
P-value...................: 0.0004131063
R^2.......................: 0.19969
Adjusted r^2..............: 0.182716
Sample size of AE DB......: 2388
Sample size of model......: 964
Missing data %............: 59.63149
- processing SMC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Med.Statin.LLD + GFR_MDRD + Peripheral.interv + stenose +
IL6_pg_ug_2015_rank, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age Med.Statin.LLDyes GFR_MDRD
0.011860 0.141020 -0.006095 -0.324391 -0.002439
Peripheral.interv stenose50-70% stenose70-90% stenose90-99% stenose100% (Occlusion)
0.248431 0.551901 0.793695 0.908083 0.443882
stenose50-99% stenose70-99% IL6_pg_ug_2015_rank
0.493076 -0.034770 0.380503
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose +
IL6_pg_ug_2015_rank, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.46700 -0.63753 -0.00906 0.56995 3.03699
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.062669 0.618832 -0.101 0.91936
currentDF[, TRAIT] 0.140772 0.031152 4.519 7.01e-06 ***
Age -0.006146 0.003699 -1.662 0.09693 .
Gendermale -0.009755 0.065906 -0.148 0.88236
Hypertension.compositeyes 0.107174 0.089522 1.197 0.23154
DiabetesStatusDiabetes -0.073263 0.071166 -1.029 0.30352
SmokerCurrentyes 0.007015 0.065595 0.107 0.91486
Med.Statin.LLDyes -0.325020 0.071302 -4.558 5.84e-06 ***
Med.all.antiplateletyes 0.060764 0.096746 0.628 0.53011
GFR_MDRD -0.002494 0.001587 -1.572 0.11630
BMI -0.002236 0.008283 -0.270 0.78724
CAD_history -0.020546 0.067180 -0.306 0.75980
Stroke_history 0.006106 0.062756 0.097 0.92251
Peripheral.interv 0.251735 0.078277 3.216 0.00134 **
stenose50-70% 0.572437 0.465907 1.229 0.21951
stenose70-90% 0.814682 0.452975 1.799 0.07242 .
stenose90-99% 0.923564 0.452837 2.040 0.04168 *
stenose100% (Occlusion) 0.470060 0.553315 0.850 0.39580
stenose50-99% 0.482530 0.778970 0.619 0.53577
stenose70-99% -0.010458 0.581872 -0.018 0.98566
IL6_pg_ug_2015_rank 0.380070 0.029873 12.723 < 2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8966 on 939 degrees of freedom
Multiple R-squared: 0.2058, Adjusted R-squared: 0.1889
F-statistic: 12.17 on 20 and 939 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_rank ' with ' SMC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6R_pg_ug_2015_rank
Trait/outcome.............: SMC_rank
Effect size...............: 0.140772
Standard error............: 0.031152
Odds ratio (effect size)..: 1.151
Lower 95% CI..............: 1.083
Upper 95% CI..............: 1.224
T-value...................: 4.518919
P-value...................: 7.008123e-06
R^2.......................: 0.205808
Adjusted r^2..............: 0.188892
Sample size of AE DB......: 2388
Sample size of model......: 960
Missing data %............: 59.79899
- processing VesselDensity_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Med.Statin.LLD + GFR_MDRD + Peripheral.interv + stenose +
IL6_pg_ug_2015_rank, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age Med.Statin.LLDyes GFR_MDRD
0.140681 0.082137 -0.009974 -0.325309 -0.002484
Peripheral.interv stenose50-70% stenose70-90% stenose90-99% stenose100% (Occlusion)
0.245491 0.685825 0.948039 1.053760 0.595197
stenose50-99% stenose70-99% IL6_pg_ug_2015_rank
0.649564 0.200915 0.361521
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose +
IL6_pg_ug_2015_rank, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.43521 -0.61488 -0.03731 0.57256 2.95720
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.1224769 0.6354987 0.193 0.84722
currentDF[, TRAIT] 0.0838526 0.0315461 2.658 0.00800 **
Age -0.0099916 0.0038138 -2.620 0.00895 **
Gendermale -0.0701012 0.0679394 -1.032 0.30244
Hypertension.compositeyes 0.1130800 0.0934662 1.210 0.22666
DiabetesStatusDiabetes -0.0899933 0.0760878 -1.183 0.23723
SmokerCurrentyes -0.0001862 0.0688784 -0.003 0.99784
Med.Statin.LLDyes -0.3216068 0.0737798 -4.359 1.46e-05 ***
Med.all.antiplateletyes 0.0254133 0.1024816 0.248 0.80421
GFR_MDRD -0.0024681 0.0016782 -1.471 0.14174
BMI -0.0013735 0.0086073 -0.160 0.87326
CAD_history -0.0400880 0.0705725 -0.568 0.57015
Stroke_history -0.0009273 0.0657583 -0.014 0.98875
Peripheral.interv 0.2442989 0.0830145 2.943 0.00334 **
stenose50-70% 0.7075552 0.4734750 1.494 0.13543
stenose70-90% 0.9615837 0.4584673 2.097 0.03624 *
stenose90-99% 1.0614252 0.4579661 2.318 0.02070 *
stenose100% (Occlusion) 0.6114586 0.5598014 1.092 0.27501
stenose50-99% 0.6547144 0.7870557 0.832 0.40572
stenose70-99% 0.2400900 0.6446638 0.372 0.70967
IL6_pg_ug_2015_rank 0.3607180 0.0307143 11.744 < 2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9068 on 878 degrees of freedom
Multiple R-squared: 0.1948, Adjusted R-squared: 0.1765
F-statistic: 10.62 on 20 and 878 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_rank ' with ' VesselDensity_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6R_pg_ug_2015_rank
Trait/outcome.............: VesselDensity_rank
Effect size...............: 0.083853
Standard error............: 0.031546
Odds ratio (effect size)..: 1.087
Lower 95% CI..............: 1.022
Upper 95% CI..............: 1.157
T-value...................: 2.658102
P-value...................: 0.008001116
R^2.......................: 0.194845
Adjusted r^2..............: 0.176505
Sample size of AE DB......: 2388
Sample size of model......: 899
Missing data %............: 62.35343
Analysis of MCP1_pg_ug_2015_rank.
- processing Macrophages_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Med.Statin.LLD +
stenose + IL6_pg_ug_2015_rank, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Med.Statin.LLDyes stenose50-70% stenose70-90%
-0.16111 -0.10144 -0.09883 0.33742 0.30463
stenose90-99% stenose100% (Occlusion) stenose50-99% stenose70-99% IL6_pg_ug_2015_rank
0.17735 -0.39888 0.80246 0.61182 0.47786
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose +
IL6_pg_ug_2015_rank, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.26224 -0.64187 -0.09494 0.63236 2.77380
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.0672862 0.5684358 -0.118 0.905798
currentDF[, TRAIT] -0.1047256 0.0285550 -3.668 0.000258 ***
Age -0.0000102 0.0035026 -0.003 0.997676
Gendermale 0.0684751 0.0625268 1.095 0.273730
Hypertension.compositeyes -0.1160757 0.0870115 -1.334 0.182506
DiabetesStatusDiabetes -0.0335692 0.0687372 -0.488 0.625398
SmokerCurrentyes -0.0498207 0.0631379 -0.789 0.430259
Med.Statin.LLDyes -0.1017569 0.0689928 -1.475 0.140565
Med.all.antiplateletyes -0.0223070 0.0933974 -0.239 0.811280
GFR_MDRD 0.0005390 0.0015035 0.358 0.720054
BMI -0.0014093 0.0078166 -0.180 0.856961
CAD_history 0.0272658 0.0647784 0.421 0.673915
Stroke_history 0.0331656 0.0607674 0.546 0.585343
Peripheral.interv 0.0861408 0.0746907 1.153 0.249070
stenose50-70% 0.2914923 0.4112495 0.709 0.478620
stenose70-90% 0.2756693 0.3977808 0.693 0.488463
stenose90-99% 0.1523826 0.3975690 0.383 0.701591
stenose100% (Occlusion) -0.4551263 0.5056162 -0.900 0.368267
stenose50-99% 0.7737669 0.7385155 1.048 0.295022
stenose70-99% 0.5838603 0.5356743 1.090 0.276003
IL6_pg_ug_2015_rank 0.4787064 0.0287679 16.640 < 2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.881 on 974 degrees of freedom
Multiple R-squared: 0.24, Adjusted R-squared: 0.2244
F-statistic: 15.38 on 20 and 974 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' Macrophages_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: Macrophages_rank
Effect size...............: -0.104726
Standard error............: 0.028555
Odds ratio (effect size)..: 0.901
Lower 95% CI..............: 0.852
Upper 95% CI..............: 0.952
T-value...................: -3.667503
P-value...................: 0.0002581885
R^2.......................: 0.239978
Adjusted r^2..............: 0.224372
Sample size of AE DB......: 2388
Sample size of model......: 995
Missing data %............: 58.33333
- processing SMC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Med.Statin.LLD + IL6_pg_ug_2015_rank,
data = currentDF)
Coefficients:
(Intercept) Med.Statin.LLDyes IL6_pg_ug_2015_rank
0.09823 -0.11500 0.46568
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose +
IL6_pg_ug_2015_rank, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.33826 -0.64411 -0.09313 0.64652 2.70649
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.0850675 0.5739917 -0.148 0.8822
currentDF[, TRAIT] -0.0211859 0.0303105 -0.699 0.4847
Age 0.0006778 0.0035764 0.190 0.8497
Gendermale 0.0454935 0.0639174 0.712 0.4768
Hypertension.compositeyes -0.1192407 0.0877809 -1.358 0.1747
DiabetesStatusDiabetes -0.0316131 0.0693538 -0.456 0.6486
SmokerCurrentyes -0.0387297 0.0637439 -0.608 0.5436
Med.Statin.LLDyes -0.1167283 0.0697194 -1.674 0.0944 .
Med.all.antiplateletyes -0.0130987 0.0942061 -0.139 0.8894
GFR_MDRD 0.0004897 0.0015188 0.322 0.7472
BMI -0.0014803 0.0078944 -0.188 0.8513
CAD_history 0.0226080 0.0654141 0.346 0.7297
Stroke_history 0.0246322 0.0612970 0.402 0.6879
Peripheral.interv 0.0951718 0.0757309 1.257 0.2092
stenose50-70% 0.3062128 0.4148356 0.738 0.4606
stenose70-90% 0.2699157 0.4013374 0.673 0.5014
stenose90-99% 0.1487390 0.4011992 0.371 0.7109
stenose100% (Occlusion) -0.4130198 0.5100737 -0.810 0.4183
stenose50-99% 0.7988378 0.7454035 1.072 0.2841
stenose70-99% 0.5793423 0.5403134 1.072 0.2839
IL6_pg_ug_2015_rank 0.4667285 0.0292849 15.937 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8883 on 970 degrees of freedom
Multiple R-squared: 0.2298, Adjusted R-squared: 0.214
F-statistic: 14.47 on 20 and 970 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' SMC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: SMC_rank
Effect size...............: -0.021186
Standard error............: 0.030311
Odds ratio (effect size)..: 0.979
Lower 95% CI..............: 0.923
Upper 95% CI..............: 1.039
T-value...................: -0.698961
P-value...................: 0.4847442
R^2.......................: 0.229831
Adjusted r^2..............: 0.213951
Sample size of AE DB......: 2388
Sample size of model......: 991
Missing data %............: 58.50084
- processing VesselDensity_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Med.Statin.LLD +
IL6_pg_ug_2015_rank, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Med.Statin.LLDyes IL6_pg_ug_2015_rank
0.1059 -0.1129 -0.1340 0.4638
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose +
IL6_pg_ug_2015_rank, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.2114 -0.6422 -0.1143 0.6644 2.8347
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.1123560 0.5830692 -0.193 0.847239
currentDF[, TRAIT] -0.1087557 0.0302044 -3.601 0.000335 ***
Age 0.0013327 0.0036430 0.366 0.714580
Gendermale 0.0812631 0.0651062 1.248 0.212293
Hypertension.compositeyes -0.1166860 0.0906341 -1.287 0.198269
DiabetesStatusDiabetes -0.0222591 0.0732448 -0.304 0.761274
SmokerCurrentyes -0.0389012 0.0661475 -0.588 0.556613
Med.Statin.LLDyes -0.1356726 0.0713394 -1.902 0.057515 .
Med.all.antiplateletyes 0.0298707 0.0985617 0.303 0.761909
GFR_MDRD 0.0004300 0.0015827 0.272 0.785909
BMI -0.0008913 0.0081026 -0.110 0.912433
CAD_history 0.0326170 0.0679685 0.480 0.631426
Stroke_history 0.0385906 0.0635076 0.608 0.543569
Peripheral.interv 0.0770844 0.0794949 0.970 0.332466
stenose50-70% 0.1688723 0.4175091 0.404 0.685958
stenose70-90% 0.1751473 0.4021881 0.435 0.663313
stenose90-99% 0.0669127 0.4016549 0.167 0.867728
stenose100% (Occlusion) -0.4970748 0.5107978 -0.973 0.330746
stenose50-99% 0.7449020 0.7452457 1.000 0.317800
stenose70-99% 0.3718812 0.5989682 0.621 0.534841
IL6_pg_ug_2015_rank 0.4673873 0.0298681 15.648 < 2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8889 on 908 degrees of freedom
Multiple R-squared: 0.245, Adjusted R-squared: 0.2284
F-statistic: 14.73 on 20 and 908 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' VesselDensity_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: VesselDensity_rank
Effect size...............: -0.108756
Standard error............: 0.030204
Odds ratio (effect size)..: 0.897
Lower 95% CI..............: 0.845
Upper 95% CI..............: 0.952
T-value...................: -3.600662
P-value...................: 0.0003346057
R^2.......................: 0.244999
Adjusted r^2..............: 0.228369
Sample size of AE DB......: 2388
Sample size of model......: 929
Missing data %............: 61.09715 cat("Edit the column names...\n")Edit the column names...colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
"Beta", "s.e.m.",
"OR", "low95CI", "up95CI",
"T-value", "P-value", "r^2", "r^2_adj", "AE_N", "Model_N", "Perc_Miss")
cat("Correct the variable types...\n")Correct the variable types...GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)
# Save the data
cat("Writing results to Excel-file...\n")Writing results to Excel-file...### Univariate
library(openxlsx)
write.xlsx(GLM.results,
file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Con.Multi.Protein.PlaquePhenotypes.RANK.MODEL5.xlsx"),
row.names = FALSE, col.names = TRUE, sheetName = "Con.Multi.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")Removing intermediate files...rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)
Analysis of binary plaque traits as a function of serum/plaque IL6(R)/MCP1 levels.
GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
PROTEIN = TRAITS.PROTEIN.RANK[protein]
cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
for (trait in 1:length(TRAITS.BIN)) {
TRAIT = TRAITS.BIN[trait]
cat(paste0("\n- processing ",TRAIT,"\n\n"))
currentDF <- as.data.frame(AEDB.CEA %>%
dplyr::select(., PROTEIN, TRAIT, COVARIATES_M5rank) %>%
filter(complete.cases(.))) %>%
filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
# for debug
# print(DT::datatable(currentDF))
# print(nrow(currentDF))
# print(str(currentDF))
# print(class(currentDF[,TRAIT]))
### univariate
fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender +
Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose + IL6_pg_ug_2015_rank,
data = currentDF, family = binomial(link = "logit"))
model_step <- stepAIC(fit, direction = "both", trace = FALSE)
print(model_step)
print(summary(fit))
GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
GLM.results = rbind(GLM.results, GLM.results.TEMP)
}
}
Analysis of IL6_rank.
- processing CalcificationPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Hypertension.composite + DiabetesStatus + GFR_MDRD + Stroke_history +
Peripheral.interv, family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Hypertension.compositeyes DiabetesStatusDiabetes GFR_MDRD
1.1271 0.2358 0.4947 -0.4908 -0.0128
Stroke_history Peripheral.interv
-0.4847 -0.4380
Degrees of Freedom: 370 Total (i.e. Null); 364 Residual
Null Deviance: 506.1
Residual Deviance: 485.6 AIC: 499.6
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + IL6_pg_ug_2015_rank, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.0991 -1.2044 0.7779 1.0251 1.7551
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.154564 2.239780 -0.069 0.9450
currentDF[, PROTEIN] 0.262668 0.119003 2.207 0.0273 *
Age 0.008433 0.014091 0.598 0.5495
Gendermale -0.197170 0.250644 -0.787 0.4315
Hypertension.compositeyes 0.582892 0.344647 1.691 0.0908 .
DiabetesStatusDiabetes -0.485547 0.280208 -1.733 0.0831 .
SmokerCurrentyes 0.051832 0.240725 0.215 0.8295
Med.Statin.LLDyes 0.156202 0.254201 0.614 0.5389
Med.all.antiplateletyes 0.445866 0.396461 1.125 0.2608
GFR_MDRD -0.013723 0.006473 -2.120 0.0340 *
BMI -0.007941 0.031195 -0.255 0.7991
CAD_history -0.191094 0.251677 -0.759 0.4477
Stroke_history -0.543313 0.236626 -2.296 0.0217 *
Peripheral.interv -0.428498 0.283220 -1.513 0.1303
stenose50-70% 1.078224 1.554409 0.694 0.4879
stenose70-90% 0.798269 1.450927 0.550 0.5822
stenose90-99% 0.422362 1.443382 0.293 0.7698
stenose100% (Occlusion) 1.759182 1.913401 0.919 0.3579
IL6_pg_ug_2015_rank 0.009689 0.109895 0.088 0.9297
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 506.13 on 370 degrees of freedom
Residual deviance: 478.11 on 352 degrees of freedom
AIC: 516.11
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_rank ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_rank
Trait/outcome.............: CalcificationPlaque
Effect size...............: 0.262668
Standard error............: 0.119003
Odds ratio (effect size)..: 1.3
Lower 95% CI..............: 1.03
Upper 95% CI..............: 1.642
Z-value...................: 2.207238
P-value...................: 0.02729742
Hosmer and Lemeshow r^2...: 0.055356
Cox and Snell r^2.........: 0.072738
Nagelkerke's pseudo r^2...: 0.09771
Sample size of AE DB......: 2388
Sample size of model......: 371
Missing data %............: 84.46399
- processing CollagenPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ DiabetesStatus +
Med.all.antiplatelet + Peripheral.interv + stenose, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) DiabetesStatusDiabetes Med.all.antiplateletyes Peripheral.interv stenose50-70%
16.9670 0.5050 0.8794 -0.5262 -0.2168
stenose70-90% stenose90-99% stenose100% (Occlusion)
-16.2556 -16.5665 0.4065
Degrees of Freedom: 370 Total (i.e. Null); 363 Residual
Null Deviance: 376.2
Residual Deviance: 356.9 AIC: 372.9
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + IL6_pg_ug_2015_rank, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.20699 0.00025 0.60086 0.71577 1.25310
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.714e+01 2.759e+03 0.006 0.9950
currentDF[, PROTEIN] -1.061e-01 1.409e-01 -0.753 0.4514
Age 8.344e-03 1.736e-02 0.481 0.6307
Gendermale -1.127e-01 3.051e-01 -0.369 0.7118
Hypertension.compositeyes 9.511e-02 4.163e-01 0.228 0.8193
DiabetesStatusDiabetes 5.811e-01 3.727e-01 1.559 0.1190
SmokerCurrentyes 3.757e-01 3.027e-01 1.241 0.2145
Med.Statin.LLDyes 8.815e-02 3.041e-01 0.290 0.7719
Med.all.antiplateletyes 8.317e-01 4.272e-01 1.947 0.0516 .
GFR_MDRD -2.607e-03 7.804e-03 -0.334 0.7383
BMI -2.999e-02 3.876e-02 -0.774 0.4390
CAD_history 5.258e-03 3.037e-01 0.017 0.9862
Stroke_history 2.934e-01 2.987e-01 0.982 0.3260
Peripheral.interv -5.299e-01 3.212e-01 -1.649 0.0991 .
stenose50-70% -2.547e-01 2.949e+03 0.000 0.9999
stenose70-90% -1.625e+01 2.759e+03 -0.006 0.9953
stenose90-99% -1.660e+01 2.759e+03 -0.006 0.9952
stenose100% (Occlusion) 3.289e-01 3.355e+03 0.000 0.9999
IL6_pg_ug_2015_rank -7.294e-02 1.321e-01 -0.552 0.5807
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 376.24 on 370 degrees of freedom
Residual deviance: 351.74 on 352 degrees of freedom
AIC: 389.74
Number of Fisher Scoring iterations: 16
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_rank ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_rank
Trait/outcome.............: CollagenPlaque
Effect size...............: -0.106139
Standard error............: 0.140924
Odds ratio (effect size)..: 0.899
Lower 95% CI..............: 0.682
Upper 95% CI..............: 1.185
Z-value...................: -0.753162
P-value...................: 0.4513528
Hosmer and Lemeshow r^2...: 0.065117
Cox and Snell r^2.........: 0.063903
Nagelkerke's pseudo r^2...: 0.100275
Sample size of AE DB......: 2388
Sample size of model......: 371
Missing data %............: 84.46399
- processing Fat10Perc
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Gender + Hypertension.composite +
DiabetesStatus + Stroke_history + IL6_pg_ug_2015_rank, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) Gendermale Hypertension.compositeyes DiabetesStatusDiabetes Stroke_history
0.2000 0.9806 0.7552 -0.4913 0.6354
IL6_pg_ug_2015_rank
0.4079
Degrees of Freedom: 370 Total (i.e. Null); 365 Residual
Null Deviance: 353.4
Residual Deviance: 326 AIC: 338
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + IL6_pg_ug_2015_rank, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.6745 0.3340 0.5000 0.6573 1.1769
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.379e+01 1.025e+03 0.013 0.9893
currentDF[, PROTEIN] 4.877e-02 1.532e-01 0.318 0.7502
Age -7.928e-03 1.821e-02 -0.435 0.6632
Gendermale 1.073e+00 3.031e-01 3.540 0.0004 ***
Hypertension.compositeyes 7.769e-01 4.116e-01 1.887 0.0591 .
DiabetesStatusDiabetes -5.348e-01 3.469e-01 -1.542 0.1231
SmokerCurrentyes 1.027e-02 3.150e-01 0.033 0.9740
Med.Statin.LLDyes -3.361e-01 3.461e-01 -0.971 0.3315
Med.all.antiplateletyes 9.334e-02 5.259e-01 0.177 0.8591
GFR_MDRD -3.589e-03 8.498e-03 -0.422 0.6727
BMI 2.784e-02 3.917e-02 0.711 0.4773
CAD_history -1.927e-01 3.279e-01 -0.588 0.5568
Stroke_history 6.793e-01 3.342e-01 2.033 0.0421 *
Peripheral.interv -4.333e-02 3.557e-01 -0.122 0.9031
stenose50-70% -1.420e+01 1.025e+03 -0.014 0.9889
stenose70-90% -1.320e+01 1.025e+03 -0.013 0.9897
stenose90-99% -1.343e+01 1.025e+03 -0.013 0.9895
stenose100% (Occlusion) -1.409e+01 1.025e+03 -0.014 0.9890
IL6_pg_ug_2015_rank 3.747e-01 1.477e-01 2.537 0.0112 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 353.45 on 370 degrees of freedom
Residual deviance: 320.00 on 352 degrees of freedom
AIC: 358
Number of Fisher Scoring iterations: 14
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_rank ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_rank
Trait/outcome.............: Fat10Perc
Effect size...............: 0.048772
Standard error............: 0.15322
Odds ratio (effect size)..: 1.05
Lower 95% CI..............: 0.778
Upper 95% CI..............: 1.418
Z-value...................: 0.318315
P-value...................: 0.7502463
Hosmer and Lemeshow r^2...: 0.094637
Cox and Snell r^2.........: 0.086215
Nagelkerke's pseudo r^2...: 0.140347
Sample size of AE DB......: 2388
Sample size of model......: 371
Missing data %............: 84.46399
- processing IPH
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + Gender +
Hypertension.composite + DiabetesStatus + BMI, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) Age Gendermale Hypertension.compositeyes DiabetesStatusDiabetes
-3.56538 0.03604 0.60110 0.50040 -0.49360
BMI
0.05614
Degrees of Freedom: 370 Total (i.e. Null); 365 Residual
Null Deviance: 426.3
Residual Deviance: 406.6 AIC: 418.6
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + IL6_pg_ug_2015_rank, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.1170 -1.0237 0.6345 0.7939 1.3578
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 10.536573 623.105263 0.017 0.9865
currentDF[, PROTEIN] -0.072502 0.131246 -0.552 0.5807
Age 0.029878 0.015474 1.931 0.0535 .
Gendermale 0.653322 0.269334 2.426 0.0153 *
Hypertension.compositeyes 0.511240 0.360309 1.419 0.1559
DiabetesStatusDiabetes -0.533832 0.304331 -1.754 0.0794 .
SmokerCurrentyes 0.072614 0.273203 0.266 0.7904
Med.Statin.LLDyes -0.071926 0.288455 -0.249 0.8031
Med.all.antiplateletyes -0.469526 0.510870 -0.919 0.3581
GFR_MDRD -0.007508 0.007295 -1.029 0.3034
BMI 0.053981 0.034966 1.544 0.1226
CAD_history 0.071455 0.292926 0.244 0.8073
Stroke_history 0.115620 0.268209 0.431 0.6664
Peripheral.interv 0.380643 0.336888 1.130 0.2585
stenose50-70% -12.627468 623.102557 -0.020 0.9838
stenose70-90% -12.933723 623.102256 -0.021 0.9834
stenose90-99% -12.724102 623.102241 -0.020 0.9837
stenose100% (Occlusion) -12.949079 623.103565 -0.021 0.9834
IL6_pg_ug_2015_rank 0.138068 0.123533 1.118 0.2637
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 426.33 on 370 degrees of freedom
Residual deviance: 399.37 on 352 degrees of freedom
AIC: 437.37
Number of Fisher Scoring iterations: 13
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_rank ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_rank
Trait/outcome.............: IPH
Effect size...............: -0.072502
Standard error............: 0.131246
Odds ratio (effect size)..: 0.93
Lower 95% CI..............: 0.719
Upper 95% CI..............: 1.203
Z-value...................: -0.552416
P-value...................: 0.5806632
Hosmer and Lemeshow r^2...: 0.063237
Cox and Snell r^2.........: 0.070091
Nagelkerke's pseudo r^2...: 0.102608
Sample size of AE DB......: 2388
Sample size of model......: 371
Missing data %............: 84.46399
Analysis of MCP1_rank.
- processing CalcificationPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ DiabetesStatus +
GFR_MDRD + Stroke_history + Peripheral.interv, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) DiabetesStatusDiabetes GFR_MDRD Stroke_history Peripheral.interv
1.64937 -0.62191 -0.01276 -0.48929 -0.51466
Degrees of Freedom: 394 Total (i.e. Null); 390 Residual
Null Deviance: 536.2
Residual Deviance: 518.9 AIC: 528.9
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + IL6_pg_ug_2015_rank, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.7775 -1.2137 0.8015 1.0022 1.7098
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.678314 2.208953 -0.307 0.7588
currentDF[, PROTEIN] -0.126457 0.116030 -1.090 0.2758
Age 0.014811 0.013613 1.088 0.2766
Gendermale -0.198359 0.241132 -0.823 0.4107
Hypertension.compositeyes 0.451555 0.321305 1.405 0.1599
DiabetesStatusDiabetes -0.684685 0.270340 -2.533 0.0113 *
SmokerCurrentyes 0.150746 0.233998 0.644 0.5194
Med.Statin.LLDyes -0.049299 0.249435 -0.198 0.8433
Med.all.antiplateletyes 0.464052 0.381434 1.217 0.2238
GFR_MDRD -0.011041 0.006211 -1.778 0.0754 .
BMI 0.005518 0.029397 0.188 0.8511
CAD_history -0.047750 0.248409 -0.192 0.8476
Stroke_history -0.528106 0.227312 -2.323 0.0202 *
Peripheral.interv -0.523032 0.283894 -1.842 0.0654 .
stenose50-70% 0.615739 1.616633 0.381 0.7033
stenose70-90% 0.607629 1.524056 0.399 0.6901
stenose90-99% 0.287230 1.517562 0.189 0.8499
stenose100% (Occlusion) 1.446116 2.003565 0.722 0.4704
IL6_pg_ug_2015_rank 0.040114 0.107454 0.373 0.7089
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 536.17 on 394 degrees of freedom
Residual deviance: 510.01 on 376 degrees of freedom
AIC: 548.01
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: CalcificationPlaque
Effect size...............: -0.126457
Standard error............: 0.11603
Odds ratio (effect size)..: 0.881
Lower 95% CI..............: 0.702
Upper 95% CI..............: 1.106
Z-value...................: -1.089862
P-value...................: 0.2757739
Hosmer and Lemeshow r^2...: 0.048787
Cox and Snell r^2.........: 0.064078
Nagelkerke's pseudo r^2...: 0.086281
Sample size of AE DB......: 2388
Sample size of model......: 395
Missing data %............: 83.45896
- processing CollagenPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
DiabetesStatus + SmokerCurrent + Med.all.antiplatelet + Peripheral.interv +
stenose, family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] DiabetesStatusDiabetes SmokerCurrentyes Med.all.antiplateletyes
16.8023 -0.5037 0.5604 0.4183 1.0655
Peripheral.interv stenose50-70% stenose70-90% stenose90-99% stenose100% (Occlusion)
-0.4970 -0.3808 -16.1722 -16.6993 0.2163
Degrees of Freedom: 394 Total (i.e. Null); 385 Residual
Null Deviance: 398.1
Residual Deviance: 361.1 AIC: 381.1
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + IL6_pg_ug_2015_rank, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.35134 0.00035 0.53023 0.70233 1.35973
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.727e+01 2.697e+03 0.006 0.994891
currentDF[, PROTEIN] -4.904e-01 1.489e-01 -3.295 0.000985 ***
Age 6.444e-03 1.743e-02 0.370 0.711618
Gendermale -1.050e-01 3.052e-01 -0.344 0.730951
Hypertension.compositeyes 1.497e-01 3.978e-01 0.376 0.706610
DiabetesStatusDiabetes 6.344e-01 3.780e-01 1.678 0.093348 .
SmokerCurrentyes 3.929e-01 2.993e-01 1.313 0.189327
Med.Statin.LLDyes 1.101e-01 3.004e-01 0.367 0.713880
Med.all.antiplateletyes 1.033e+00 4.268e-01 2.421 0.015469 *
GFR_MDRD -2.116e-03 7.736e-03 -0.274 0.784408
BMI -4.011e-02 3.882e-02 -1.033 0.301595
CAD_history 2.136e-03 3.022e-01 0.007 0.994360
Stroke_history 3.252e-01 2.944e-01 1.105 0.269293
Peripheral.interv -4.911e-01 3.356e-01 -1.463 0.143391
stenose50-70% -2.739e-01 2.884e+03 0.000 0.999924
stenose70-90% -1.607e+01 2.697e+03 -0.006 0.995245
stenose90-99% -1.661e+01 2.697e+03 -0.006 0.995085
stenose100% (Occlusion) 1.666e-01 3.255e+03 0.000 0.999959
IL6_pg_ug_2015_rank -4.145e-02 1.334e-01 -0.311 0.756111
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 398.07 on 394 degrees of freedom
Residual deviance: 357.98 on 376 degrees of freedom
AIC: 395.98
Number of Fisher Scoring iterations: 16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: CollagenPlaque
Effect size...............: -0.490423
Standard error............: 0.148854
Odds ratio (effect size)..: 0.612
Lower 95% CI..............: 0.457
Upper 95% CI..............: 0.82
Z-value...................: -3.294667
P-value...................: 0.0009853822
Hosmer and Lemeshow r^2...: 0.100723
Cox and Snell r^2.........: 0.096525
Nagelkerke's pseudo r^2...: 0.152014
Sample size of AE DB......: 2388
Sample size of model......: 395
Missing data %............: 83.45896
- processing Fat10Perc
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Gender + Hypertension.composite + BMI + Stroke_history +
IL6_pg_ug_2015_rank, family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Gendermale Hypertension.compositeyes BMI
-0.97678 0.49301 0.74809 0.68441 0.05152
Stroke_history IL6_pg_ug_2015_rank
0.58867 0.27994
Degrees of Freedom: 394 Total (i.e. Null); 388 Residual
Null Deviance: 372.1
Residual Deviance: 334.3 AIC: 348.3
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + IL6_pg_ug_2015_rank, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.6698 0.2914 0.4662 0.6372 1.6612
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 12.380042 986.394897 0.013 0.98999
currentDF[, PROTEIN] 0.502902 0.159989 3.143 0.00167 **
Age 0.004448 0.017753 0.251 0.80218
Gendermale 0.807662 0.298791 2.703 0.00687 **
Hypertension.compositeyes 0.801928 0.398141 2.014 0.04399 *
DiabetesStatusDiabetes -0.240743 0.350078 -0.688 0.49165
SmokerCurrentyes 0.028747 0.311494 0.092 0.92647
Med.Statin.LLDyes -0.213231 0.346371 -0.616 0.53815
Med.all.antiplateletyes -0.145093 0.527572 -0.275 0.78330
GFR_MDRD 0.001898 0.008388 0.226 0.82099
BMI 0.049489 0.039064 1.267 0.20520
CAD_history -0.250635 0.335123 -0.748 0.45453
Stroke_history 0.594527 0.326713 1.820 0.06880 .
Peripheral.interv 0.096042 0.374004 0.257 0.79734
stenose50-70% -14.663779 986.392765 -0.015 0.98814
stenose70-90% -13.156167 986.392596 -0.013 0.98936
stenose90-99% -13.579957 986.392567 -0.014 0.98902
stenose100% (Occlusion) -13.881091 986.393384 -0.014 0.98877
IL6_pg_ug_2015_rank 0.255792 0.145952 1.753 0.07967 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 372.10 on 394 degrees of freedom
Residual deviance: 325.86 on 376 degrees of freedom
AIC: 363.86
Number of Fisher Scoring iterations: 14
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: Fat10Perc
Effect size...............: 0.502902
Standard error............: 0.159989
Odds ratio (effect size)..: 1.654
Lower 95% CI..............: 1.208
Upper 95% CI..............: 2.263
Z-value...................: 3.143363
P-value...................: 0.001670184
Hosmer and Lemeshow r^2...: 0.124259
Cox and Snell r^2.........: 0.110463
Nagelkerke's pseudo r^2...: 0.181039
Sample size of AE DB......: 2388
Sample size of model......: 395
Missing data %............: 83.45896
- processing IPH
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + Gender +
DiabetesStatus + BMI, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) Age Gendermale DiabetesStatusDiabetes BMI
-2.83856 0.03202 0.58621 -0.48794 0.05657
Degrees of Freedom: 394 Total (i.e. Null); 390 Residual
Null Deviance: 449.1
Residual Deviance: 432.8 AIC: 442.8
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + IL6_pg_ug_2015_rank, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.1176 -0.9779 0.6353 0.7916 1.5456
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 10.805144 617.904988 0.017 0.9860
currentDF[, PROTEIN] 0.049050 0.131219 0.374 0.7085
Age 0.023740 0.015089 1.573 0.1156
Gendermale 0.577668 0.259736 2.224 0.0261 *
Hypertension.compositeyes 0.428341 0.343972 1.245 0.2130
DiabetesStatusDiabetes -0.525303 0.294582 -1.783 0.0746 .
SmokerCurrentyes -0.017901 0.264461 -0.068 0.9460
Med.Statin.LLDyes -0.090069 0.283967 -0.317 0.7511
Med.all.antiplateletyes -0.224785 0.452574 -0.497 0.6194
GFR_MDRD -0.005685 0.007069 -0.804 0.4212
BMI 0.049055 0.033139 1.480 0.1388
CAD_history 0.158330 0.293685 0.539 0.5898
Stroke_history 0.241351 0.261579 0.923 0.3562
Peripheral.interv 0.372192 0.343255 1.084 0.2782
stenose50-70% -12.662747 617.902663 -0.020 0.9837
stenose70-90% -12.874037 617.902369 -0.021 0.9834
stenose90-99% -12.670408 617.902351 -0.021 0.9836
stenose100% (Occlusion) -12.761985 617.903615 -0.021 0.9835
IL6_pg_ug_2015_rank 0.086998 0.121825 0.714 0.4752
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 449.12 on 394 degrees of freedom
Residual deviance: 424.14 on 376 degrees of freedom
AIC: 462.14
Number of Fisher Scoring iterations: 13
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: IPH
Effect size...............: 0.04905
Standard error............: 0.131219
Odds ratio (effect size)..: 1.05
Lower 95% CI..............: 0.812
Upper 95% CI..............: 1.358
Z-value...................: 0.373805
P-value...................: 0.7085493
Hosmer and Lemeshow r^2...: 0.055615
Cox and Snell r^2.........: 0.061277
Nagelkerke's pseudo r^2...: 0.090216
Sample size of AE DB......: 2388
Sample size of model......: 395
Missing data %............: 83.45896
Analysis of IL6_pg_ug_2015_rank.
- processing CalcificationPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + SmokerCurrent + CAD_history + stenose, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Age SmokerCurrentyes CAD_history
-1.21906 -0.10139 0.01999 0.39841 0.25581
stenose50-70% stenose70-90% stenose90-99% stenose100% (Occlusion) stenose50-99%
-1.00567 -0.50697 -0.26246 0.80569 -13.93538
stenose70-99%
-1.53003
Degrees of Freedom: 996 Total (i.e. Null); 986 Residual
Null Deviance: 1381
Residual Deviance: 1349 AIC: 1371
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + IL6_pg_ug_2015_rank, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.6147 -1.1333 -0.7964 1.1564 1.6722
Coefficients: (1 not defined because of singularities)
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.909944 1.321284 -0.689 0.49102
currentDF[, PROTEIN] -0.099976 0.066163 -1.511 0.13077
Age 0.017028 0.008100 2.102 0.03552 *
Gendermale -0.132850 0.144146 -0.922 0.35672
Hypertension.compositeyes 0.232383 0.202519 1.147 0.25119
DiabetesStatusDiabetes -0.175024 0.159524 -1.097 0.27257
SmokerCurrentyes 0.413549 0.146703 2.819 0.00482 **
Med.Statin.LLDyes -0.164639 0.159153 -1.034 0.30092
Med.all.antiplateletyes -0.223519 0.217193 -1.029 0.30342
GFR_MDRD -0.001873 0.003492 -0.536 0.59169
BMI 0.012482 0.018095 0.690 0.49034
CAD_history 0.264314 0.149945 1.763 0.07795 .
Stroke_history -0.138429 0.140758 -0.983 0.32538
Peripheral.interv -0.183619 0.172919 -1.062 0.28829
stenose50-70% -0.939975 0.962505 -0.977 0.32877
stenose70-90% -0.485844 0.928836 -0.523 0.60093
stenose90-99% -0.236697 0.928355 -0.255 0.79875
stenose100% (Occlusion) 0.801826 1.244197 0.644 0.51928
stenose50-99% -14.007873 368.424975 -0.038 0.96967
stenose70-99% -1.453878 1.253623 -1.160 0.24615
IL6_pg_ug_2015_rank NA NA NA NA
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1381.3 on 996 degrees of freedom
Residual deviance: 1340.3 on 977 degrees of freedom
AIC: 1380.3
Number of Fisher Scoring iterations: 12
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_rank ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_pg_ug_2015_rank
Trait/outcome.............: CalcificationPlaque
Effect size...............: -0.099976
Standard error............: 0.066163
Odds ratio (effect size)..: 0.905
Lower 95% CI..............: 0.795
Upper 95% CI..............: 1.03
Z-value...................: -1.511065
P-value...................: 0.1307718
Hosmer and Lemeshow r^2...: 0.029706
Cox and Snell r^2.........: 0.04032
Nagelkerke's pseudo r^2...: 0.053776
Sample size of AE DB......: 2388
Sample size of model......: 997
Missing data %............: 58.24958
- processing CollagenPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Hypertension.composite + SmokerCurrent + BMI + Stroke_history,
family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Hypertension.compositeyes SmokerCurrentyes BMI
-0.01981 -0.29027 0.34009 0.44475 0.03349
Stroke_history
0.25226
Degrees of Freedom: 999 Total (i.e. Null); 994 Residual
Null Deviance: 1017
Residual Deviance: 993 AIC: 1005
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + IL6_pg_ug_2015_rank, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.2838 0.4520 0.6083 0.7205 1.0945
Coefficients: (1 not defined because of singularities)
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.292e+01 6.481e+02 0.020 0.984089
currentDF[, PROTEIN] -2.788e-01 8.257e-02 -3.376 0.000735 ***
Age 8.333e-03 9.876e-03 0.844 0.398752
Gendermale -8.397e-02 1.781e-01 -0.471 0.637300
Hypertension.compositeyes 3.107e-01 2.330e-01 1.334 0.182365
DiabetesStatusDiabetes 2.105e-01 2.039e-01 1.033 0.301764
SmokerCurrentyes 4.917e-01 1.863e-01 2.639 0.008305 **
Med.Statin.LLDyes -1.661e-02 1.955e-01 -0.085 0.932280
Med.all.antiplateletyes 1.316e-01 2.617e-01 0.503 0.615014
GFR_MDRD 4.714e-03 4.302e-03 1.096 0.273176
BMI 3.356e-02 2.363e-02 1.420 0.155532
CAD_history 2.203e-01 1.882e-01 1.171 0.241679
Stroke_history 2.405e-01 1.765e-01 1.363 0.172966
Peripheral.interv -1.820e-02 2.155e-01 -0.084 0.932671
stenose50-70% -1.361e+01 6.481e+02 -0.021 0.983249
stenose70-90% -1.401e+01 6.481e+02 -0.022 0.982747
stenose90-99% -1.406e+01 6.481e+02 -0.022 0.982690
stenose100% (Occlusion) 4.945e-01 8.183e+02 0.001 0.999518
stenose50-99% 1.313e-02 1.208e+03 0.000 0.999991
stenose70-99% -1.373e+01 6.481e+02 -0.021 0.983103
IL6_pg_ug_2015_rank NA NA NA NA
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1017.22 on 999 degrees of freedom
Residual deviance: 980.18 on 980 degrees of freedom
AIC: 1020.2
Number of Fisher Scoring iterations: 14
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_rank ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_pg_ug_2015_rank
Trait/outcome.............: CollagenPlaque
Effect size...............: -0.278762
Standard error............: 0.08257
Odds ratio (effect size)..: 0.757
Lower 95% CI..............: 0.644
Upper 95% CI..............: 0.89
Z-value...................: -3.376059
P-value...................: 0.0007353204
Hosmer and Lemeshow r^2...: 0.036411
Cox and Snell r^2.........: 0.036361
Nagelkerke's pseudo r^2...: 0.056956
Sample size of AE DB......: 2388
Sample size of model......: 1000
Missing data %............: 58.12395
- processing Fat10Perc
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Gender + Stroke_history + Peripheral.interv, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Gendermale Stroke_history Peripheral.interv
0.5014 0.4505 0.8327 0.3722 -0.6087
Degrees of Freedom: 999 Total (i.e. Null); 995 Residual
Null Deviance: 1165
Residual Deviance: 1079 AIC: 1089
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + IL6_pg_ug_2015_rank, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.2290 -1.0455 0.6047 0.8064 2.0491
Coefficients: (1 not defined because of singularities)
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.317e+01 3.905e+02 0.034 0.973093
currentDF[, PROTEIN] 4.532e-01 7.890e-02 5.744 9.26e-09 ***
Age 9.621e-03 9.328e-03 1.031 0.302330
Gendermale 8.512e-01 1.611e-01 5.282 1.27e-07 ***
Hypertension.compositeyes 6.829e-02 2.349e-01 0.291 0.771250
DiabetesStatusDiabetes -1.155e-01 1.839e-01 -0.628 0.529991
SmokerCurrentyes 1.014e-01 1.710e-01 0.593 0.553366
Med.Statin.LLDyes -1.768e-01 1.904e-01 -0.928 0.353200
Med.all.antiplateletyes 6.371e-02 2.501e-01 0.255 0.798916
GFR_MDRD -2.636e-04 4.098e-03 -0.064 0.948718
BMI 3.776e-03 2.044e-02 0.185 0.853460
CAD_history 7.889e-02 1.751e-01 0.451 0.652297
Stroke_history 3.768e-01 1.703e-01 2.213 0.026912 *
Peripheral.interv -6.136e-01 1.860e-01 -3.299 0.000972 ***
stenose50-70% -1.353e+01 3.905e+02 -0.035 0.972366
stenose70-90% -1.348e+01 3.905e+02 -0.035 0.972456
stenose90-99% -1.333e+01 3.905e+02 -0.034 0.972772
stenose100% (Occlusion) -1.427e+01 3.905e+02 -0.037 0.970836
stenose50-99% -1.498e+01 3.905e+02 -0.038 0.969387
stenose70-99% -1.461e+01 3.905e+02 -0.037 0.970150
IL6_pg_ug_2015_rank NA NA NA NA
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1164.5 on 999 degrees of freedom
Residual deviance: 1067.6 on 980 degrees of freedom
AIC: 1107.6
Number of Fisher Scoring iterations: 13
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_rank ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_pg_ug_2015_rank
Trait/outcome.............: Fat10Perc
Effect size...............: 0.453209
Standard error............: 0.078905
Odds ratio (effect size)..: 1.573
Lower 95% CI..............: 1.348
Upper 95% CI..............: 1.837
Z-value...................: 5.743761
P-value...................: 9.259659e-09
Hosmer and Lemeshow r^2...: 0.083261
Cox and Snell r^2.........: 0.092407
Nagelkerke's pseudo r^2...: 0.134327
Sample size of AE DB......: 2388
Sample size of model......: 1000
Missing data %............: 58.12395
- processing IPH
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Gender + Med.Statin.LLD +
BMI + CAD_history + Stroke_history + stenose, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) Gendermale Med.Statin.LLDyes BMI CAD_history
0.16028 0.59271 -0.25963 0.02923 0.30318
Stroke_history stenose50-70% stenose70-90% stenose90-99% stenose100% (Occlusion)
0.25028 -1.09743 -0.97086 -0.65159 -0.80087
stenose50-99% stenose70-99%
-15.27559 0.59126
Degrees of Freedom: 998 Total (i.e. Null); 987 Residual
Null Deviance: 1331
Residual Deviance: 1288 AIC: 1312
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + IL6_pg_ug_2015_rank, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.9367 -1.2695 0.8101 0.9793 1.4492
Coefficients: (1 not defined because of singularities)
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.134237 1.491544 0.090 0.9283
currentDF[, PROTEIN] 0.054522 0.068254 0.799 0.4244
Age 0.001619 0.008307 0.195 0.8455
Gendermale 0.638876 0.146508 4.361 1.3e-05 ***
Hypertension.compositeyes -0.108080 0.207201 -0.522 0.6019
DiabetesStatusDiabetes -0.123290 0.163538 -0.754 0.4509
SmokerCurrentyes 0.124934 0.151554 0.824 0.4097
Med.Statin.LLDyes -0.249499 0.167274 -1.492 0.1358
Med.all.antiplateletyes 0.161931 0.221057 0.733 0.4638
GFR_MDRD -0.004974 0.003614 -1.376 0.1687
BMI 0.033028 0.018696 1.767 0.0773 .
CAD_history 0.310960 0.157200 1.978 0.0479 *
Stroke_history 0.230621 0.146500 1.574 0.1154
Peripheral.interv 0.037076 0.178283 0.208 0.8353
stenose50-70% -1.011322 1.165248 -0.868 0.3854
stenose70-90% -0.904406 1.139736 -0.794 0.4275
stenose90-99% -0.590355 1.139754 -0.518 0.6045
stenose100% (Occlusion) -0.735447 1.357428 -0.542 0.5880
stenose50-99% -15.239630 376.951258 -0.040 0.9678
stenose70-99% 0.597045 1.580966 0.378 0.7057
IL6_pg_ug_2015_rank NA NA NA NA
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1331.0 on 998 degrees of freedom
Residual deviance: 1283.4 on 979 degrees of freedom
AIC: 1323.4
Number of Fisher Scoring iterations: 12
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_rank ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_pg_ug_2015_rank
Trait/outcome.............: IPH
Effect size...............: 0.054522
Standard error............: 0.068254
Odds ratio (effect size)..: 1.056
Lower 95% CI..............: 0.924
Upper 95% CI..............: 1.207
Z-value...................: 0.798812
P-value...................: 0.4243993
Hosmer and Lemeshow r^2...: 0.03579
Cox and Snell r^2.........: 0.046565
Nagelkerke's pseudo r^2...: 0.063256
Sample size of AE DB......: 2388
Sample size of model......: 999
Missing data %............: 58.16583
Analysis of IL6R_pg_ug_2015_rank.
- processing CalcificationPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + SmokerCurrent +
CAD_history + stenose + IL6_pg_ug_2015_rank, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) Age SmokerCurrentyes CAD_history stenose50-70%
-0.30345 0.01695 0.35191 0.23377 -1.63626
stenose70-90% stenose90-99% stenose100% (Occlusion) stenose50-99% stenose70-99%
-1.19502 -0.95911 0.12013 -14.62719 -2.19118
IL6_pg_ug_2015_rank
-0.10793
Degrees of Freedom: 964 Total (i.e. Null); 954 Residual
Null Deviance: 1337
Residual Deviance: 1308 AIC: 1330
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + IL6_pg_ug_2015_rank, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.6427 -1.1294 -0.8126 1.1605 1.6993
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.365860 1.506287 0.243 0.8081
currentDF[, PROTEIN] 0.035856 0.073333 0.489 0.6249
Age 0.012807 0.008312 1.541 0.1233
Gendermale -0.137973 0.147315 -0.937 0.3490
Hypertension.compositeyes 0.232166 0.204392 1.136 0.2560
DiabetesStatusDiabetes -0.220703 0.162318 -1.360 0.1739
SmokerCurrentyes 0.354163 0.149199 2.374 0.0176 *
Med.Statin.LLDyes -0.155029 0.163322 -0.949 0.3425
Med.all.antiplateletyes -0.281243 0.221170 -1.272 0.2035
GFR_MDRD -0.002650 0.003626 -0.731 0.4648
BMI 0.005013 0.018788 0.267 0.7896
CAD_history 0.249201 0.152590 1.633 0.1024
Stroke_history -0.138158 0.142661 -0.968 0.3328
Peripheral.interv -0.198377 0.177818 -1.116 0.2646
stenose50-70% -1.511542 1.195399 -1.264 0.2061
stenose70-90% -1.140150 1.168415 -0.976 0.3292
stenose90-99% -0.897700 1.168584 -0.768 0.4424
stenose100% (Occlusion) 0.140360 1.431129 0.098 0.9219
stenose50-99% -14.655351 369.458835 -0.040 0.9684
stenose70-99% -2.056345 1.439602 -1.428 0.1532
IL6_pg_ug_2015_rank -0.122276 0.071999 -1.698 0.0895 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1336.9 on 964 degrees of freedom
Residual deviance: 1298.8 on 944 degrees of freedom
AIC: 1340.8
Number of Fisher Scoring iterations: 12
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_rank ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6R_pg_ug_2015_rank
Trait/outcome.............: CalcificationPlaque
Effect size...............: 0.035856
Standard error............: 0.073333
Odds ratio (effect size)..: 1.037
Lower 95% CI..............: 0.898
Upper 95% CI..............: 1.197
Z-value...................: 0.488951
P-value...................: 0.6248767
Hosmer and Lemeshow r^2...: 0.028479
Cox and Snell r^2.........: 0.038686
Nagelkerke's pseudo r^2...: 0.051597
Sample size of AE DB......: 2388
Sample size of model......: 965
Missing data %............: 59.58962
- processing CollagenPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Hypertension.composite +
SmokerCurrent + IL6_pg_ug_2015_rank, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) Hypertension.compositeyes SmokerCurrentyes IL6_pg_ug_2015_rank
0.9035 0.3851 0.4154 -0.2787
Degrees of Freedom: 967 Total (i.e. Null); 964 Residual
Null Deviance: 988.9
Residual Deviance: 969.5 AIC: 977.5
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + IL6_pg_ug_2015_rank, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.1999 0.4510 0.6138 0.7199 1.1595
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.319e+01 7.229e+02 0.018 0.985446
currentDF[, PROTEIN] 9.488e-02 9.151e-02 1.037 0.299848
Age 8.079e-03 1.012e-02 0.798 0.424662
Gendermale -3.242e-02 1.808e-01 -0.179 0.857720
Hypertension.compositeyes 3.106e-01 2.344e-01 1.325 0.185201
DiabetesStatusDiabetes 2.597e-01 2.081e-01 1.248 0.212027
SmokerCurrentyes 4.712e-01 1.888e-01 2.496 0.012569 *
Med.Statin.LLDyes 5.820e-03 2.001e-01 0.029 0.976800
Med.all.antiplateletyes 1.787e-01 2.642e-01 0.676 0.498818
GFR_MDRD 3.823e-03 4.435e-03 0.862 0.388617
BMI 2.593e-02 2.430e-02 1.067 0.285857
CAD_history 2.617e-01 1.921e-01 1.363 0.173018
Stroke_history 2.485e-01 1.784e-01 1.393 0.163631
Peripheral.interv -6.451e-02 2.204e-01 -0.293 0.769737
stenose50-70% -1.371e+01 7.229e+02 -0.019 0.984872
stenose70-90% -1.411e+01 7.229e+02 -0.020 0.984424
stenose90-99% -1.415e+01 7.229e+02 -0.020 0.984388
stenose100% (Occlusion) 4.788e-01 8.767e+02 0.001 0.999564
stenose50-99% -6.751e-02 1.250e+03 0.000 0.999957
stenose70-99% -1.374e+01 7.229e+02 -0.019 0.984839
IL6_pg_ug_2015_rank -3.146e-01 9.023e-02 -3.487 0.000488 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 988.95 on 967 degrees of freedom
Residual deviance: 952.48 on 947 degrees of freedom
AIC: 994.48
Number of Fisher Scoring iterations: 14
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_rank ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6R_pg_ug_2015_rank
Trait/outcome.............: CollagenPlaque
Effect size...............: 0.094877
Standard error............: 0.091513
Odds ratio (effect size)..: 1.1
Lower 95% CI..............: 0.919
Upper 95% CI..............: 1.316
Z-value...................: 1.03676
P-value...................: 0.2998478
Hosmer and Lemeshow r^2...: 0.03687
Cox and Snell r^2.........: 0.036967
Nagelkerke's pseudo r^2...: 0.057762
Sample size of AE DB......: 2388
Sample size of model......: 968
Missing data %............: 59.46399
- processing Fat10Perc
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Gender + Stroke_history +
Peripheral.interv + IL6_pg_ug_2015_rank, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) Gendermale Stroke_history Peripheral.interv IL6_pg_ug_2015_rank
0.5335 0.7864 0.3842 -0.6161 0.4560
Degrees of Freedom: 967 Total (i.e. Null); 963 Residual
Null Deviance: 1120
Residual Deviance: 1040 AIC: 1050
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + IL6_pg_ug_2015_rank, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.2182 -1.0330 0.6043 0.7938 1.9895
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.301e+01 4.386e+02 0.030 0.9763
currentDF[, PROTEIN] -9.866e-02 8.461e-02 -1.166 0.2436
Age 1.064e-02 9.678e-03 1.100 0.2714
Gendermale 7.990e-01 1.655e-01 4.827 1.38e-06 ***
Hypertension.compositeyes 5.719e-02 2.396e-01 0.239 0.8113
DiabetesStatusDiabetes -7.140e-02 1.882e-01 -0.379 0.7044
SmokerCurrentyes 1.314e-01 1.751e-01 0.750 0.4532
Med.Statin.LLDyes -1.867e-01 1.959e-01 -0.953 0.3405
Med.all.antiplateletyes 7.462e-02 2.551e-01 0.293 0.7699
GFR_MDRD 3.978e-04 4.299e-03 0.093 0.9263
BMI -8.401e-04 2.143e-02 -0.039 0.9687
CAD_history 7.036e-02 1.788e-01 0.393 0.6940
Stroke_history 3.898e-01 1.732e-01 2.250 0.0245 *
Peripheral.interv -5.916e-01 1.915e-01 -3.090 0.0020 **
stenose50-70% -1.334e+01 4.386e+02 -0.030 0.9757
stenose70-90% -1.330e+01 4.386e+02 -0.030 0.9758
stenose90-99% -1.313e+01 4.386e+02 -0.030 0.9761
stenose100% (Occlusion) -1.411e+01 4.386e+02 -0.032 0.9743
stenose50-99% -1.479e+01 4.386e+02 -0.034 0.9731
stenose70-99% -1.454e+01 4.386e+02 -0.033 0.9736
IL6_pg_ug_2015_rank 4.952e-01 8.634e-02 5.735 9.76e-09 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1120.4 on 967 degrees of freedom
Residual deviance: 1028.3 on 947 degrees of freedom
AIC: 1070.3
Number of Fisher Scoring iterations: 13
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_rank ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6R_pg_ug_2015_rank
Trait/outcome.............: Fat10Perc
Effect size...............: -0.098664
Standard error............: 0.084608
Odds ratio (effect size)..: 0.906
Lower 95% CI..............: 0.768
Upper 95% CI..............: 1.069
Z-value...................: -1.166137
P-value...................: 0.2435592
Hosmer and Lemeshow r^2...: 0.082253
Cox and Snell r^2.........: 0.090813
Nagelkerke's pseudo r^2...: 0.132436
Sample size of AE DB......: 2388
Sample size of model......: 968
Missing data %............: 59.46399
- processing IPH
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Gender + GFR_MDRD + CAD_history, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Gendermale GFR_MDRD CAD_history
0.36958 0.12120 0.61093 -0.00555 0.26123
Degrees of Freedom: 966 Total (i.e. Null); 962 Residual
Null Deviance: 1288
Residual Deviance: 1260 AIC: 1270
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + IL6_pg_ug_2015_rank, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.9270 -1.2740 0.8058 0.9810 1.4859
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.658791 1.540117 0.428 0.6688
currentDF[, PROTEIN] 0.096366 0.075830 1.271 0.2038
Age -0.002405 0.008572 -0.281 0.7791
Gendermale 0.653345 0.149866 4.360 1.3e-05 ***
Hypertension.compositeyes -0.113781 0.209329 -0.544 0.5867
DiabetesStatusDiabetes -0.166184 0.165938 -1.001 0.3166
SmokerCurrentyes 0.102067 0.154554 0.660 0.5090
Med.Statin.LLDyes -0.207534 0.171488 -1.210 0.2262
Med.all.antiplateletyes 0.101285 0.225730 0.449 0.6536
GFR_MDRD -0.006024 0.003767 -1.599 0.1098
BMI 0.021536 0.019381 1.111 0.2665
CAD_history 0.339149 0.160490 2.113 0.0346 *
Stroke_history 0.168929 0.148040 1.141 0.2538
Peripheral.interv 0.005009 0.183636 0.027 0.9782
stenose50-70% -0.712137 1.212748 -0.587 0.5571
stenose70-90% -0.725992 1.187466 -0.611 0.5409
stenose90-99% -0.434821 1.188064 -0.366 0.7144
stenose100% (Occlusion) -0.560313 1.397228 -0.401 0.6884
stenose50-99% -15.047936 375.974826 -0.040 0.9681
stenose70-99% 0.870277 1.613226 0.539 0.5896
IL6_pg_ug_2015_rank 0.019307 0.074389 0.260 0.7952
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1287.7 on 966 degrees of freedom
Residual deviance: 1242.2 on 946 degrees of freedom
AIC: 1284.2
Number of Fisher Scoring iterations: 12
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_rank ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6R_pg_ug_2015_rank
Trait/outcome.............: IPH
Effect size...............: 0.096366
Standard error............: 0.07583
Odds ratio (effect size)..: 1.101
Lower 95% CI..............: 0.949
Upper 95% CI..............: 1.278
Z-value...................: 1.270818
P-value...................: 0.2037934
Hosmer and Lemeshow r^2...: 0.035318
Cox and Snell r^2.........: 0.045942
Nagelkerke's pseudo r^2...: 0.062425
Sample size of AE DB......: 2388
Sample size of model......: 967
Missing data %............: 59.50586
Analysis of MCP1_pg_ug_2015_rank.
- processing CalcificationPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + SmokerCurrent + Med.Statin.LLD + CAD_history + IL6_pg_ug_2015_rank,
family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Age SmokerCurrentyes Med.Statin.LLDyes CAD_history
-1.51542 -0.59564 0.02046 0.42977 -0.24147 0.28995
IL6_pg_ug_2015_rank
0.18445
Degrees of Freedom: 995 Total (i.e. Null); 989 Residual
Null Deviance: 1380
Residual Deviance: 1300 AIC: 1314
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + IL6_pg_ug_2015_rank, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.880 -1.075 -0.661 1.089 2.137
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -1.109170 1.345267 -0.824 0.40966
currentDF[, PROTEIN] -0.570914 0.079317 -7.198 6.11e-13 ***
Age 0.019220 0.008374 2.295 0.02171 *
Gendermale -0.117423 0.148383 -0.791 0.42874
Hypertension.compositeyes 0.173225 0.208445 0.831 0.40595
DiabetesStatusDiabetes -0.198676 0.164371 -1.209 0.22677
SmokerCurrentyes 0.423323 0.151405 2.796 0.00517 **
Med.Statin.LLDyes -0.233261 0.164455 -1.418 0.15608
Med.all.antiplateletyes -0.244111 0.222935 -1.095 0.27352
GFR_MDRD -0.001316 0.003604 -0.365 0.71495
BMI 0.012374 0.018631 0.664 0.50658
CAD_history 0.294365 0.154806 1.902 0.05724 .
Stroke_history -0.135470 0.144686 -0.936 0.34912
Peripheral.interv -0.134033 0.177869 -0.754 0.45112
stenose50-70% -0.797920 0.966890 -0.825 0.40923
stenose70-90% -0.350437 0.931769 -0.376 0.70684
stenose90-99% -0.163779 0.931216 -0.176 0.86039
stenose100% (Occlusion) 0.579291 1.254446 0.462 0.64423
stenose50-99% -13.596961 357.337235 -0.038 0.96965
stenose70-99% -1.058369 1.267398 -0.835 0.40368
IL6_pg_ug_2015_rank 0.166581 0.077275 2.156 0.03111 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1379.8 on 995 degrees of freedom
Residual deviance: 1283.6 on 975 degrees of freedom
AIC: 1325.6
Number of Fisher Scoring iterations: 12
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: CalcificationPlaque
Effect size...............: -0.570914
Standard error............: 0.079317
Odds ratio (effect size)..: 0.565
Lower 95% CI..............: 0.484
Upper 95% CI..............: 0.66
Z-value...................: -7.197909
P-value...................: 6.114291e-13
Hosmer and Lemeshow r^2...: 0.069762
Cox and Snell r^2.........: 0.092123
Nagelkerke's pseudo r^2...: 0.122868
Sample size of AE DB......: 2388
Sample size of model......: 996
Missing data %............: 58.29146
- processing CollagenPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
SmokerCurrent + BMI + Stroke_history + IL6_pg_ug_2015_rank,
family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] SmokerCurrentyes BMI Stroke_history IL6_pg_ug_2015_rank
0.19963 -0.16122 0.41058 0.03702 0.25107 -0.22211
Degrees of Freedom: 998 Total (i.e. Null); 993 Residual
Null Deviance: 1017
Residual Deviance: 991.7 AIC: 1004
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + IL6_pg_ug_2015_rank, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.2982 0.4429 0.6106 0.7208 1.1427
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.286e+01 6.472e+02 0.020 0.98415
currentDF[, PROTEIN] -1.566e-01 9.042e-02 -1.732 0.08322 .
Age 8.602e-03 9.904e-03 0.869 0.38509
Gendermale -8.042e-02 1.785e-01 -0.451 0.65235
Hypertension.compositeyes 2.960e-01 2.331e-01 1.269 0.20427
DiabetesStatusDiabetes 2.102e-01 2.042e-01 1.029 0.30338
SmokerCurrentyes 4.921e-01 1.868e-01 2.635 0.00843 **
Med.Statin.LLDyes -2.945e-02 1.963e-01 -0.150 0.88076
Med.all.antiplateletyes 1.282e-01 2.617e-01 0.490 0.62412
GFR_MDRD 4.973e-03 4.316e-03 1.152 0.24916
BMI 3.439e-02 2.382e-02 1.444 0.14880
CAD_history 2.187e-01 1.887e-01 1.159 0.24633
Stroke_history 2.435e-01 1.767e-01 1.378 0.16806
Peripheral.interv -5.055e-03 2.160e-01 -0.023 0.98132
stenose50-70% -1.356e+01 6.472e+02 -0.021 0.98329
stenose70-90% -1.397e+01 6.472e+02 -0.022 0.98277
stenose90-99% -1.404e+01 6.472e+02 -0.022 0.98270
stenose100% (Occlusion) 4.210e-01 8.183e+02 0.001 0.99959
stenose50-99% 1.436e-01 1.205e+03 0.000 0.99990
stenose70-99% -1.359e+01 6.472e+02 -0.021 0.98324
IL6_pg_ug_2015_rank -2.065e-01 9.187e-02 -2.248 0.02457 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1016.76 on 998 degrees of freedom
Residual deviance: 976.91 on 978 degrees of freedom
AIC: 1018.9
Number of Fisher Scoring iterations: 14
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: CollagenPlaque
Effect size...............: -0.156637
Standard error............: 0.090422
Odds ratio (effect size)..: 0.855
Lower 95% CI..............: 0.716
Upper 95% CI..............: 1.021
Z-value...................: -1.732282
P-value...................: 0.08322335
Hosmer and Lemeshow r^2...: 0.039191
Cox and Snell r^2.........: 0.039102
Nagelkerke's pseudo r^2...: 0.061231
Sample size of AE DB......: 2388
Sample size of model......: 999
Missing data %............: 58.16583
- processing Fat10Perc
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Gender + Stroke_history +
Peripheral.interv + IL6_pg_ug_2015_rank, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) Gendermale Stroke_history Peripheral.interv IL6_pg_ug_2015_rank
0.5001 0.8299 0.3752 -0.6046 0.4578
Degrees of Freedom: 998 Total (i.e. Null); 994 Residual
Null Deviance: 1164
Residual Deviance: 1077 AIC: 1087
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + IL6_pg_ug_2015_rank, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.2481 -1.0386 0.6040 0.7981 2.0958
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 13.129786 391.540184 0.034 0.97325
currentDF[, PROTEIN] -0.086857 0.085197 -1.019 0.30797
Age 0.010120 0.009342 1.083 0.27870
Gendermale 0.853643 0.161410 5.289 1.23e-07 ***
Hypertension.compositeyes 0.055646 0.235533 0.236 0.81323
DiabetesStatusDiabetes -0.116612 0.184074 -0.634 0.52640
SmokerCurrentyes 0.099393 0.171329 0.580 0.56183
Med.Statin.LLDyes -0.172314 0.191205 -0.901 0.36748
Med.all.antiplateletyes 0.056685 0.250340 0.226 0.82086
GFR_MDRD -0.000110 0.004104 -0.027 0.97863
BMI 0.003309 0.020461 0.162 0.87151
CAD_history 0.073364 0.175377 0.418 0.67571
Stroke_history 0.379259 0.170276 2.227 0.02593 *
Peripheral.interv -0.601792 0.186366 -3.229 0.00124 **
stenose50-70% -13.495567 391.538791 -0.034 0.97250
stenose70-90% -13.455871 391.538696 -0.034 0.97258
stenose90-99% -13.316587 391.538695 -0.034 0.97287
stenose100% (Occlusion) -14.307713 391.539436 -0.037 0.97085
stenose50-99% -14.904510 391.541255 -0.038 0.96963
stenose70-99% -14.542576 391.539632 -0.037 0.97037
IL6_pg_ug_2015_rank 0.501944 0.089901 5.583 2.36e-08 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1163.9 on 998 degrees of freedom
Residual deviance: 1065.4 on 978 degrees of freedom
AIC: 1107.4
Number of Fisher Scoring iterations: 13
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: Fat10Perc
Effect size...............: -0.086857
Standard error............: 0.085197
Odds ratio (effect size)..: 0.917
Lower 95% CI..............: 0.776
Upper 95% CI..............: 1.083
Z-value...................: -1.019483
P-value...................: 0.3079737
Hosmer and Lemeshow r^2...: 0.084585
Cox and Snell r^2.........: 0.093846
Nagelkerke's pseudo r^2...: 0.136385
Sample size of AE DB......: 2388
Sample size of model......: 999
Missing data %............: 58.16583
- processing IPH
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Gender + Med.Statin.LLD + BMI + CAD_history + Stroke_history +
stenose + IL6_pg_ug_2015_rank, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Gendermale Med.Statin.LLDyes BMI
0.13101 -0.22463 0.61172 -0.30216 0.02976
CAD_history Stroke_history stenose50-70% stenose70-90% stenose90-99%
0.33142 0.24256 -1.02994 -0.92698 -0.62001
stenose100% (Occlusion) stenose50-99% stenose70-99% IL6_pg_ug_2015_rank
-0.92397 -15.13616 0.81161 0.15649
Degrees of Freedom: 997 Total (i.e. Null); 984 Residual
Null Deviance: 1329
Residual Deviance: 1276 AIC: 1304
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + IL6_pg_ug_2015_rank, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.9893 -1.2497 0.7839 0.9752 1.6000
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.190713 1.500984 0.127 0.89889
currentDF[, PROTEIN] -0.227337 0.076272 -2.981 0.00288 **
Age 0.001017 0.008366 0.122 0.90327
Gendermale 0.661185 0.147632 4.479 7.51e-06 ***
Hypertension.compositeyes -0.135490 0.208469 -0.650 0.51574
DiabetesStatusDiabetes -0.139422 0.164394 -0.848 0.39639
SmokerCurrentyes 0.110583 0.152610 0.725 0.46869
Med.Statin.LLDyes -0.296861 0.169050 -1.756 0.07908 .
Med.all.antiplateletyes 0.164792 0.222329 0.741 0.45857
GFR_MDRD -0.005186 0.003636 -1.426 0.15374
BMI 0.033370 0.018886 1.767 0.07723 .
CAD_history 0.332543 0.158490 2.098 0.03589 *
Stroke_history 0.236477 0.147225 1.606 0.10822
Peripheral.interv 0.050247 0.179428 0.280 0.77945
stenose50-70% -0.958270 1.172731 -0.817 0.41386
stenose70-90% -0.855005 1.147066 -0.745 0.45604
stenose90-99% -0.556029 1.147046 -0.485 0.62785
stenose100% (Occlusion) -0.839142 1.364150 -0.615 0.53846
stenose50-99% -15.103152 374.531247 -0.040 0.96783
stenose70-99% 0.785866 1.596121 0.492 0.62247
IL6_pg_ug_2015_rank 0.151521 0.077747 1.949 0.05131 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1329.1 on 997 degrees of freedom
Residual deviance: 1271.5 on 977 degrees of freedom
AIC: 1313.5
Number of Fisher Scoring iterations: 12
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: IPH
Effect size...............: -0.227337
Standard error............: 0.076272
Odds ratio (effect size)..: 0.797
Lower 95% CI..............: 0.686
Upper 95% CI..............: 0.925
Z-value...................: -2.98059
P-value...................: 0.002876934
Hosmer and Lemeshow r^2...: 0.043363
Cox and Snell r^2.........: 0.056113
Nagelkerke's pseudo r^2...: 0.076242
Sample size of AE DB......: 2388
Sample size of model......: 998
Missing data %............: 58.2077 cat("Edit the column names...\n")Edit the column names...colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
"Beta", "s.e.m.",
"OR", "low95CI", "up95CI",
"Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")
cat("Correct the variable types...\n")Correct the variable types...GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)
# Save the data
cat("Writing results to Excel-file...\n")Writing results to Excel-file...### Univariate
write.xlsx(GLM.results,
file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Multi.Protein.PlaquePhenotypes.RANK.MODEL5.xlsx"),
row.names = FALSE, col.names = TRUE, sheetName = "Bin.Multi.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")Removing intermediate files...rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)
B. Cross-sectional analysis symptoms
We will perform a cross-sectional analysis between plaque and serum MCP1, IL6, and IL6R levels and the ‘clinical status’ of the plaque in terms of presence of patients’ symptoms (symptomatic vs. asymptomatic). The symptoms of interest are:
- stroke
- TIA
- retinal infarction
- amaurosis fugax
- asymptomatic
Model 1
In this model we correct for Age, and Gender.
Natural log-transformed data
First we use the natural-log transformed data.
GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN)) {
PROTEIN = TRAITS.PROTEIN[protein]
cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
TRAIT = "AsymptSympt"
cat(paste0("\n- processing ",TRAIT,"\n\n"))
currentDF <- as.data.frame(AEDB.CEA %>%
dplyr::select(., PROTEIN, TRAIT, COVARIATES_M1) %>%
filter(complete.cases(.))) %>%
filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
# for debug
# print(DT::datatable(currentDF))
# print(nrow(currentDF))
# print(str(currentDF))
# print(class(currentDF[,TRAIT]))
### univariate
# + Hypertension.composite + DiabetesStatus + SmokerCurrent +
# Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
# CAD_history + Stroke_history + Peripheral.interv + stenose
fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender,
data = currentDF, family = binomial(link = "logit"))
model_step <- stepAIC(fit, direction = "both", trace = FALSE)
print(model_step)
print(summary(fit))
GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
GLM.results = rbind(GLM.results, GLM.results.TEMP)
}
Analysis of IL6_LN.
- processing AsymptSympt
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Age, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) Age
0.11643 0.02191
Degrees of Freedom: 455 Total (i.e. Null); 454 Residual
Null Deviance: 417.3
Residual Deviance: 414.9 AIC: 418.9
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.0057 0.5380 0.5871 0.6331 0.8358
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.12920 1.08309 0.119 0.905
currentDF[, PROTEIN] -0.02445 0.11699 -0.209 0.834
Age 0.02168 0.01420 1.527 0.127
Gendermale 0.14136 0.27357 0.517 0.605
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 417.29 on 455 degrees of freedom
Residual deviance: 414.58 on 452 degrees of freedom
AIC: 422.58
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_LN ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_LN
Trait/outcome.............: AsymptSympt
Effect size...............: -0.024449
Standard error............: 0.11699
Odds ratio (effect size)..: 0.976
Lower 95% CI..............: 0.776
Upper 95% CI..............: 1.227
Z-value...................: -0.208979
P-value...................: 0.8344648
Hosmer and Lemeshow r^2...: 0.006485
Cox and Snell r^2.........: 0.005917
Nagelkerke's pseudo r^2...: 0.009869
Sample size of AE DB......: 2388
Sample size of model......: 456
Missing data %............: 80.90452
Analysis of MCP1_LN.
- processing AsymptSympt
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN],
family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN]
0.2534 0.3195
Degrees of Freedom: 555 Total (i.e. Null); 554 Residual
Null Deviance: 479
Residual Deviance: 473.6 AIC: 477.6
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.2317 0.4736 0.5421 0.6107 0.9570
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.80551 1.13385 -0.710 0.4774
currentDF[, PROTEIN] 0.34336 0.13841 2.481 0.0131 *
Age 0.01681 0.01356 1.240 0.2151
Gendermale -0.24157 0.27090 -0.892 0.3725
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 478.98 on 555 degrees of freedom
Residual deviance: 471.32 on 552 degrees of freedom
AIC: 479.32
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_LN ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_LN
Trait/outcome.............: AsymptSympt
Effect size...............: 0.343356
Standard error............: 0.138412
Odds ratio (effect size)..: 1.41
Lower 95% CI..............: 1.075
Upper 95% CI..............: 1.849
Z-value...................: 2.480677
P-value...................: 0.01311332
Hosmer and Lemeshow r^2...: 0.015985
Cox and Snell r^2.........: 0.013676
Nagelkerke's pseudo r^2...: 0.023684
Sample size of AE DB......: 2388
Sample size of model......: 556
Missing data %............: 76.71692
Analysis of IL6_pg_ug_2015_LN.
- processing AsymptSympt
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender, family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Age Gendermale
0.69140 0.10908 0.03025 -0.37729
Degrees of Freedom: 1149 Total (i.e. Null); 1146 Residual
Null Deviance: 790.7
Residual Deviance: 776.9 AIC: 784.9
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.4157 0.3916 0.4523 0.5168 0.7514
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.69140 0.73163 0.945 0.3447
currentDF[, PROTEIN] 0.10908 0.06495 1.680 0.0931 .
Age 0.03025 0.01029 2.938 0.0033 **
Gendermale -0.37729 0.22148 -1.703 0.0885 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 790.69 on 1149 degrees of freedom
Residual deviance: 776.93 on 1146 degrees of freedom
AIC: 784.93
Number of Fisher Scoring iterations: 5
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_LN ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_pg_ug_2015_LN
Trait/outcome.............: AsymptSympt
Effect size...............: 0.109082
Standard error............: 0.064949
Odds ratio (effect size)..: 1.115
Lower 95% CI..............: 0.982
Upper 95% CI..............: 1.267
Z-value...................: 1.6795
P-value...................: 0.09305457
Hosmer and Lemeshow r^2...: 0.017405
Cox and Snell r^2.........: 0.011896
Nagelkerke's pseudo r^2...: 0.023925
Sample size of AE DB......: 2388
Sample size of model......: 1150
Missing data %............: 51.84255
Analysis of IL6R_pg_ug_2015_LN.
- processing AsymptSympt
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender, family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Age Gendermale
-0.10978 -0.12672 0.03285 -0.39487
Degrees of Freedom: 1150 Total (i.e. Null); 1147 Residual
Null Deviance: 803.5
Residual Deviance: 787.8 AIC: 795.8
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.3738 0.3926 0.4565 0.5237 0.8748
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.10978 0.71558 -0.153 0.87807
currentDF[, PROTEIN] -0.12672 0.08779 -1.443 0.14889
Age 0.03285 0.01023 3.213 0.00132 **
Gendermale -0.39487 0.22099 -1.787 0.07397 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 803.47 on 1150 degrees of freedom
Residual deviance: 787.76 on 1147 degrees of freedom
AIC: 795.76
Number of Fisher Scoring iterations: 5
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_LN ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6R_pg_ug_2015_LN
Trait/outcome.............: AsymptSympt
Effect size...............: -0.126723
Standard error............: 0.08779
Odds ratio (effect size)..: 0.881
Lower 95% CI..............: 0.742
Upper 95% CI..............: 1.046
Z-value...................: -1.443477
P-value...................: 0.148886
Hosmer and Lemeshow r^2...: 0.019561
Cox and Snell r^2.........: 0.013562
Nagelkerke's pseudo r^2...: 0.026992
Sample size of AE DB......: 2388
Sample size of model......: 1151
Missing data %............: 51.80067
Analysis of MCP1_pg_ug_2015_LN.
- processing AsymptSympt
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender, family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Age Gendermale
0.46900 0.17995 0.03227 -0.43409
Degrees of Freedom: 1195 Total (i.e. Null); 1192 Residual
Null Deviance: 826.5
Residual Deviance: 805.9 AIC: 813.9
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.5337 0.3791 0.4465 0.5159 0.8508
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.46900 0.70095 0.669 0.50343
currentDF[, PROTEIN] 0.17995 0.06837 2.632 0.00848 **
Age 0.03227 0.01010 3.194 0.00140 **
Gendermale -0.43409 0.21786 -1.992 0.04632 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 826.52 on 1195 degrees of freedom
Residual deviance: 805.92 on 1192 degrees of freedom
AIC: 813.92
Number of Fisher Scoring iterations: 5
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_LN ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_LN
Trait/outcome.............: AsymptSympt
Effect size...............: 0.179955
Standard error............: 0.068365
Odds ratio (effect size)..: 1.197
Lower 95% CI..............: 1.047
Upper 95% CI..............: 1.369
Z-value...................: 2.632248
P-value...................: 0.008482191
Hosmer and Lemeshow r^2...: 0.024928
Cox and Snell r^2.........: 0.01708
Nagelkerke's pseudo r^2...: 0.03423
Sample size of AE DB......: 2388
Sample size of model......: 1196
Missing data %............: 49.91625 cat("Edit the column names...\n")Edit the column names...colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
"Beta", "s.e.m.",
"OR", "low95CI", "up95CI",
"Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")
cat("Correct the variable types...\n")Correct the variable types...GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)
# Save the data
cat("Writing results to Excel-file...\n")Writing results to Excel-file...### Univariate
write.xlsx(GLM.results,
file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Uni.Protein.Symptoms.MODEL1.xlsx"),
row.names = FALSE, col.names = TRUE, sheetName = "Bin.Uni.Symptoms")
# Removing intermediates
cat("Removing intermediate files...\n")Removing intermediate files...rm(TRAIT, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)
Inverse-rank normalization
Here we use the inverse-rank normalized data - visually this is more normally distributed.
GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
PROTEIN = TRAITS.PROTEIN.RANK[protein]
cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
TRAIT = "AsymptSympt"
cat(paste0("\n- processing ",TRAIT,"\n\n"))
currentDF <- as.data.frame(AEDB.CEA %>%
dplyr::select(., PROTEIN, TRAIT, COVARIATES_M1) %>%
filter(complete.cases(.))) %>%
filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
# for debug
# print(DT::datatable(currentDF))
# print(nrow(currentDF))
# print(str(currentDF))
# print(class(currentDF[,TRAIT]))
### univariate
# + Hypertension.composite + DiabetesStatus + SmokerCurrent +
# Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
# CAD_history + Stroke_history + Peripheral.interv + stenose
fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender,
data = currentDF, family = binomial(link = "logit"))
model_step <- stepAIC(fit, direction = "both", trace = FALSE)
print(model_step)
print(summary(fit))
GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
GLM.results = rbind(GLM.results, GLM.results.TEMP)
}
Analysis of IL6_rank.
- processing AsymptSympt
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Age, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) Age
0.03354 0.02322
Degrees of Freedom: 527 Total (i.e. Null); 526 Residual
Null Deviance: 482.2
Residual Deviance: 479 AIC: 483
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.0886 0.5322 0.5876 0.6383 0.8258
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.07050 0.88346 0.080 0.9364
currentDF[, PROTEIN] -0.04480 0.12075 -0.371 0.7106
Age 0.02320 0.01296 1.789 0.0735 .
Gendermale -0.04751 0.25974 -0.183 0.8549
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 482.18 on 527 degrees of freedom
Residual deviance: 478.81 on 524 degrees of freedom
AIC: 486.81
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_rank ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_rank
Trait/outcome.............: AsymptSympt
Effect size...............: -0.044803
Standard error............: 0.120747
Odds ratio (effect size)..: 0.956
Lower 95% CI..............: 0.755
Upper 95% CI..............: 1.212
Z-value...................: -0.37105
P-value...................: 0.7106006
Hosmer and Lemeshow r^2...: 0.006985
Cox and Snell r^2.........: 0.006359
Nagelkerke's pseudo r^2...: 0.010619
Sample size of AE DB......: 2388
Sample size of model......: 528
Missing data %............: 77.88945
Analysis of MCP1_rank.
- processing AsymptSympt
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age, family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Age
0.28675 0.37178 0.02121
Degrees of Freedom: 564 Total (i.e. Null); 562 Residual
Null Deviance: 495.5
Residual Deviance: 483.5 AIC: 489.5
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.3596 0.4499 0.5370 0.6291 0.9789
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.44218 0.89858 0.492 0.62265
currentDF[, PROTEIN] 0.38662 0.12065 3.205 0.00135 **
Age 0.02149 0.01316 1.633 0.10250
Gendermale -0.23867 0.26432 -0.903 0.36656
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 495.50 on 564 degrees of freedom
Residual deviance: 482.64 on 561 degrees of freedom
AIC: 490.64
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: AsymptSympt
Effect size...............: 0.386619
Standard error............: 0.120648
Odds ratio (effect size)..: 1.472
Lower 95% CI..............: 1.162
Upper 95% CI..............: 1.865
Z-value...................: 3.204514
P-value...................: 0.001352907
Hosmer and Lemeshow r^2...: 0.02596
Cox and Snell r^2.........: 0.022509
Nagelkerke's pseudo r^2...: 0.038546
Sample size of AE DB......: 2388
Sample size of model......: 565
Missing data %............: 76.34003
Analysis of IL6_pg_ug_2015_rank.
- processing AsymptSympt
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender, family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Age Gendermale
0.34611 0.15893 0.03016 -0.37900
Degrees of Freedom: 1149 Total (i.e. Null); 1146 Residual
Null Deviance: 790.7
Residual Deviance: 777 AIC: 785
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.4207 0.3921 0.4517 0.5173 0.7538
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.34611 0.70495 0.491 0.62345
currentDF[, PROTEIN] 0.15893 0.09515 1.670 0.09485 .
Age 0.03016 0.01029 2.930 0.00339 **
Gendermale -0.37900 0.22151 -1.711 0.08709 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 790.69 on 1149 degrees of freedom
Residual deviance: 776.96 on 1146 degrees of freedom
AIC: 784.96
Number of Fisher Scoring iterations: 5
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_rank ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_pg_ug_2015_rank
Trait/outcome.............: AsymptSympt
Effect size...............: 0.158935
Standard error............: 0.095151
Odds ratio (effect size)..: 1.172
Lower 95% CI..............: 0.973
Upper 95% CI..............: 1.413
Z-value...................: 1.670352
P-value...................: 0.09484966
Hosmer and Lemeshow r^2...: 0.017371
Cox and Snell r^2.........: 0.011873
Nagelkerke's pseudo r^2...: 0.023879
Sample size of AE DB......: 2388
Sample size of model......: 1150
Missing data %............: 51.84255
Analysis of IL6R_pg_ug_2015_rank.
- processing AsymptSympt
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + Gender, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) Age Gendermale
0.10526 0.03331 -0.38379
Degrees of Freedom: 1151 Total (i.e. Null); 1149 Residual
Null Deviance: 803.7
Residual Deviance: 790.2 AIC: 796.2
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.3792 0.3949 0.4577 0.5226 0.8703
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.14037 0.70096 0.200 0.84129
currentDF[, PROTEIN] -0.11502 0.09484 -1.213 0.22520
Age 0.03295 0.01023 3.222 0.00127 **
Gendermale -0.39123 0.22092 -1.771 0.07658 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 803.71 on 1151 degrees of freedom
Residual deviance: 788.73 on 1148 degrees of freedom
AIC: 796.73
Number of Fisher Scoring iterations: 5
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_rank ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6R_pg_ug_2015_rank
Trait/outcome.............: AsymptSympt
Effect size...............: -0.115023
Standard error............: 0.094838
Odds ratio (effect size)..: 0.891
Lower 95% CI..............: 0.74
Upper 95% CI..............: 1.073
Z-value...................: -1.212829
P-value...................: 0.2251951
Hosmer and Lemeshow r^2...: 0.018632
Cox and Snell r^2.........: 0.012915
Nagelkerke's pseudo r^2...: 0.025714
Sample size of AE DB......: 2388
Sample size of model......: 1152
Missing data %............: 51.75879
Analysis of MCP1_pg_ug_2015_rank.
- processing AsymptSympt
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender, family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Age Gendermale
0.24802 0.26381 0.03239 -0.43504
Degrees of Freedom: 1195 Total (i.e. Null); 1192 Residual
Null Deviance: 826.5
Residual Deviance: 804.7 AIC: 812.7
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.5655 0.3738 0.4440 0.5167 0.8433
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.24802 0.69361 0.358 0.72066
currentDF[, PROTEIN] 0.26381 0.09370 2.816 0.00487 **
Age 0.03239 0.01011 3.203 0.00136 **
Gendermale -0.43504 0.21791 -1.996 0.04589 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 826.52 on 1195 degrees of freedom
Residual deviance: 804.68 on 1192 degrees of freedom
AIC: 812.68
Number of Fisher Scoring iterations: 5
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: AsymptSympt
Effect size...............: 0.263812
Standard error............: 0.093697
Odds ratio (effect size)..: 1.302
Lower 95% CI..............: 1.083
Upper 95% CI..............: 1.564
Z-value...................: 2.815595
P-value...................: 0.004868694
Hosmer and Lemeshow r^2...: 0.026421
Cox and Snell r^2.........: 0.018093
Nagelkerke's pseudo r^2...: 0.036261
Sample size of AE DB......: 2388
Sample size of model......: 1196
Missing data %............: 49.91625 cat("Edit the column names...\n")Edit the column names...colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
"Beta", "s.e.m.",
"OR", "low95CI", "up95CI",
"Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")
cat("Correct the variable types...\n")Correct the variable types...GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)
# Save the data
cat("Writing results to Excel-file...\n")Writing results to Excel-file...### Univariate
write.xlsx(GLM.results,
file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Uni.Protein.RANK.Symptoms.MODEL1.xlsx"),
row.names = FALSE, col.names = TRUE, sheetName = "Bin.Uni.Symptoms")
# Removing intermediates
cat("Removing intermediate files...\n")Removing intermediate files...rm(TRAIT, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)
Model 2
In this model we correct for Age, Gender, Hypertension status, Diabetes status, current smoker status, lipid-lowering drugs (LLDs), antiplatelet medication, eGFR (MDRD), BMI, CAD history, stroke history, peripheral interventions, and stenosis..
Natural log-transformed data
First we use the natural-log transformed data.
GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN)) {
PROTEIN = TRAITS.PROTEIN[protein]
cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
TRAIT = "AsymptSympt"
cat(paste0("\n- processing ",TRAIT,"\n\n"))
currentDF <- as.data.frame(AEDB.CEA %>%
dplyr::select(., PROTEIN, TRAIT, COVARIATES_M2) %>%
filter(complete.cases(.))) %>%
filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
# for debug
# print(DT::datatable(currentDF))
# print(nrow(currentDF))
# print(str(currentDF))
# print(class(currentDF[,TRAIT]))
### univariate
fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose,
data = currentDF, family = binomial(link = "logit"))
model_step <- stepAIC(fit, direction = "both", trace = FALSE)
print(model_step)
print(summary(fit))
GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
GLM.results = rbind(GLM.results, GLM.results.TEMP)
}
Analysis of IL6_LN.
- processing AsymptSympt
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Stroke_history +
Peripheral.interv, family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) Stroke_history Peripheral.interv
1.469 1.421 -1.053
Degrees of Freedom: 413 Total (i.e. Null); 411 Residual
Null Deviance: 388.7
Residual Deviance: 356.9 AIC: 362.9
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.6144 0.2692 0.4703 0.6809 1.3036
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.684e+01 1.344e+03 0.013 0.990002
currentDF[, PROTEIN] -1.099e-01 1.287e-01 -0.854 0.393205
Age 1.544e-02 1.835e-02 0.841 0.400260
Gendermale 3.170e-02 3.179e-01 0.100 0.920584
Hypertension.compositeyes -4.657e-01 4.644e-01 -1.003 0.315945
DiabetesStatusDiabetes 4.233e-01 3.671e-01 1.153 0.248948
SmokerCurrentyes -1.316e-01 2.994e-01 -0.439 0.660340
Med.Statin.LLDyes -3.348e-01 3.379e-01 -0.991 0.321782
Med.all.antiplateletyes -4.925e-01 5.409e-01 -0.910 0.362567
GFR_MDRD 1.059e-02 8.343e-03 1.269 0.204415
BMI -1.891e-02 3.709e-02 -0.510 0.610181
CAD_history -1.165e-01 3.027e-01 -0.385 0.700375
Stroke_history 1.491e+00 3.883e-01 3.841 0.000123 ***
Peripheral.interv -1.037e+00 3.039e-01 -3.413 0.000642 ***
stenose50-70% -1.423e+01 1.344e+03 -0.011 0.991550
stenose70-90% -1.530e+01 1.344e+03 -0.011 0.990911
stenose90-99% -1.502e+01 1.344e+03 -0.011 0.991078
stenose100% (Occlusion) -1.352e-01 1.655e+03 0.000 0.999935
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 388.73 on 413 degrees of freedom
Residual deviance: 343.56 on 396 degrees of freedom
AIC: 379.56
Number of Fisher Scoring iterations: 15
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_LN ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_LN
Trait/outcome.............: AsymptSympt
Effect size...............: -0.109894
Standard error............: 0.128709
Odds ratio (effect size)..: 0.896
Lower 95% CI..............: 0.696
Upper 95% CI..............: 1.153
Z-value...................: -0.853819
P-value...................: 0.3932054
Hosmer and Lemeshow r^2...: 0.116194
Cox and Snell r^2.........: 0.103361
Nagelkerke's pseudo r^2...: 0.169731
Sample size of AE DB......: 2388
Sample size of model......: 414
Missing data %............: 82.66332
Analysis of MCP1_LN.
- processing AsymptSympt
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Stroke_history + Peripheral.interv, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Stroke_history Peripheral.interv
0.3598 0.2606 1.5912 -1.0558
Degrees of Freedom: 506 Total (i.e. Null); 503 Residual
Null Deviance: 445.4
Residual Deviance: 401.2 AIC: 409.2
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.8151 0.2361 0.4168 0.6296 1.2837
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.457e+01 1.306e+03 0.011 0.991100
currentDF[, PROTEIN] 2.898e-01 1.591e-01 1.821 0.068563 .
Age 1.607e-02 1.731e-02 0.928 0.353210
Gendermale -1.980e-01 3.040e-01 -0.651 0.514833
Hypertension.compositeyes -6.217e-01 4.585e-01 -1.356 0.175147
DiabetesStatusDiabetes 3.509e-01 3.435e-01 1.022 0.306992
SmokerCurrentyes -4.256e-02 2.845e-01 -0.150 0.881098
Med.Statin.LLDyes -9.946e-02 3.268e-01 -0.304 0.760824
Med.all.antiplateletyes -7.095e-01 5.344e-01 -1.328 0.184247
GFR_MDRD 7.080e-03 7.595e-03 0.932 0.351268
BMI 1.454e-02 3.583e-02 0.406 0.684935
CAD_history -2.658e-01 2.826e-01 -0.940 0.347088
Stroke_history 1.601e+00 3.808e-01 4.203 2.63e-05 ***
Peripheral.interv -1.057e+00 2.946e-01 -3.588 0.000333 ***
stenose50-70% -1.361e+01 1.306e+03 -0.010 0.991688
stenose70-90% -1.514e+01 1.306e+03 -0.012 0.990752
stenose90-99% -1.466e+01 1.306e+03 -0.011 0.991046
stenose100% (Occlusion) -1.079e-01 1.627e+03 0.000 0.999947
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 445.43 on 506 degrees of freedom
Residual deviance: 386.25 on 489 degrees of freedom
AIC: 422.25
Number of Fisher Scoring iterations: 15
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_LN ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_LN
Trait/outcome.............: AsymptSympt
Effect size...............: 0.289758
Standard error............: 0.159095
Odds ratio (effect size)..: 1.336
Lower 95% CI..............: 0.978
Upper 95% CI..............: 1.825
Z-value...................: 1.821291
P-value...................: 0.0685626
Hosmer and Lemeshow r^2...: 0.132863
Cox and Snell r^2.........: 0.110172
Nagelkerke's pseudo r^2...: 0.188452
Sample size of AE DB......: 2388
Sample size of model......: 507
Missing data %............: 78.76884
Analysis of IL6_pg_ug_2015_LN.
- processing AsymptSympt
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + Gender +
Med.all.antiplatelet + GFR_MDRD + Stroke_history + Peripheral.interv +
stenose, family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) Age Gendermale Med.all.antiplateletyes GFR_MDRD
15.387606 0.027762 -0.453161 -0.916880 0.008104
Stroke_history Peripheral.interv stenose50-70% stenose70-90% stenose90-99%
1.166727 -0.673292 -13.748714 -14.870639 -14.701591
stenose100% (Occlusion) stenose50-99% stenose70-99%
-0.331218 -16.941956 -0.167408
Degrees of Freedom: 1008 Total (i.e. Null); 996 Residual
Null Deviance: 707.6
Residual Deviance: 652.1 AIC: 678.1
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.8226 0.2652 0.4017 0.5590 0.9792
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.565e+01 1.027e+03 0.015 0.987838
currentDF[, PROTEIN] 4.009e-02 7.151e-02 0.561 0.575025
Age 3.157e-02 1.296e-02 2.437 0.014805 *
Gendermale -4.000e-01 2.443e-01 -1.637 0.101631
Hypertension.compositeyes -2.988e-01 3.648e-01 -0.819 0.412745
DiabetesStatusDiabetes -8.362e-03 2.509e-01 -0.033 0.973407
SmokerCurrentyes 1.551e-01 2.332e-01 0.665 0.506105
Med.Statin.LLDyes -1.772e-01 2.732e-01 -0.649 0.516525
Med.all.antiplateletyes -9.046e-01 4.485e-01 -2.017 0.043704 *
GFR_MDRD 7.273e-03 5.677e-03 1.281 0.200167
BMI 9.375e-04 2.893e-02 0.032 0.974147
CAD_history -1.784e-01 2.263e-01 -0.788 0.430588
Stroke_history 1.108e+00 2.862e-01 3.872 0.000108 ***
Peripheral.interv -6.278e-01 2.398e-01 -2.618 0.008851 **
stenose50-70% -1.368e+01 1.027e+03 -0.013 0.989373
stenose70-90% -1.486e+01 1.027e+03 -0.014 0.988456
stenose90-99% -1.469e+01 1.027e+03 -0.014 0.988585
stenose100% (Occlusion) -4.360e-01 1.306e+03 0.000 0.999734
stenose50-99% -1.691e+01 1.027e+03 -0.016 0.986861
stenose70-99% -1.378e-01 1.233e+03 0.000 0.999911
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 707.63 on 1008 degrees of freedom
Residual deviance: 648.67 on 989 degrees of freedom
AIC: 688.67
Number of Fisher Scoring iterations: 15
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_LN ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_pg_ug_2015_LN
Trait/outcome.............: AsymptSympt
Effect size...............: 0.040092
Standard error............: 0.071508
Odds ratio (effect size)..: 1.041
Lower 95% CI..............: 0.905
Upper 95% CI..............: 1.198
Z-value...................: 0.560667
P-value...................: 0.5750247
Hosmer and Lemeshow r^2...: 0.083324
Cox and Snell r^2.........: 0.056762
Nagelkerke's pseudo r^2...: 0.112607
Sample size of AE DB......: 2388
Sample size of model......: 1009
Missing data %............: 57.74707
Analysis of IL6R_pg_ug_2015_LN.
- processing AsymptSympt
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Med.all.antiplatelet + CAD_history + Stroke_history +
Peripheral.interv, family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Age Gendermale Med.all.antiplateletyes
0.79707 -0.18290 0.02791 -0.36266 -0.82533
CAD_history Stroke_history Peripheral.interv
-0.36391 1.02555 -0.46692
Degrees of Freedom: 1011 Total (i.e. Null); 1004 Residual
Null Deviance: 720.7
Residual Deviance: 675.8 AIC: 691.8
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.8326 0.2694 0.4155 0.5558 1.0215
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.533e+01 1.137e+03 0.013 0.98924
currentDF[, PROTEIN] -1.693e-01 9.430e-02 -1.795 0.07258 .
Age 3.516e-02 1.291e-02 2.722 0.00648 **
Gendermale -4.114e-01 2.428e-01 -1.695 0.09010 .
Hypertension.compositeyes -2.233e-01 3.507e-01 -0.637 0.52428
DiabetesStatusDiabetes -1.665e-02 2.474e-01 -0.067 0.94635
SmokerCurrentyes 2.696e-01 2.314e-01 1.165 0.24394
Med.Statin.LLDyes -1.900e-01 2.724e-01 -0.698 0.48540
Med.all.antiplateletyes -8.694e-01 4.488e-01 -1.937 0.05271 .
GFR_MDRD 5.162e-03 5.684e-03 0.908 0.36379
BMI -1.488e-02 2.959e-02 -0.503 0.61510
CAD_history -2.771e-01 2.230e-01 -1.243 0.21390
Stroke_history 1.020e+00 2.714e-01 3.760 0.00017 ***
Peripheral.interv -5.140e-01 2.410e-01 -2.132 0.03298 *
stenose50-70% -1.363e+01 1.137e+03 -0.012 0.99043
stenose70-90% -1.479e+01 1.137e+03 -0.013 0.98962
stenose90-99% -1.461e+01 1.137e+03 -0.013 0.98974
stenose100% (Occlusion) -3.467e-01 1.393e+03 0.000 0.99980
stenose50-99% -1.590e+01 1.137e+03 -0.014 0.98884
stenose70-99% -2.688e-01 1.327e+03 0.000 0.99984
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 720.70 on 1011 degrees of freedom
Residual deviance: 660.98 on 992 degrees of freedom
AIC: 700.98
Number of Fisher Scoring iterations: 15
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_LN ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6R_pg_ug_2015_LN
Trait/outcome.............: AsymptSympt
Effect size...............: -0.16932
Standard error............: 0.094303
Odds ratio (effect size)..: 0.844
Lower 95% CI..............: 0.702
Upper 95% CI..............: 1.016
Z-value...................: -1.795489
P-value...................: 0.07257589
Hosmer and Lemeshow r^2...: 0.08286
Cox and Snell r^2.........: 0.057302
Nagelkerke's pseudo r^2...: 0.112486
Sample size of AE DB......: 2388
Sample size of model......: 1012
Missing data %............: 57.62144
Analysis of MCP1_pg_ug_2015_LN.
- processing AsymptSympt
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Med.all.antiplatelet + GFR_MDRD + Stroke_history +
Peripheral.interv + stenose, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Age Gendermale Med.all.antiplateletyes
15.469983 0.168894 0.031530 -0.520258 -0.924441
GFR_MDRD Stroke_history Peripheral.interv stenose50-70% stenose70-90%
0.008484 1.057027 -0.575248 -13.815303 -15.001478
stenose90-99% stenose100% (Occlusion) stenose50-99% stenose70-99%
-14.758355 -0.371350 -16.106565 -0.440020
Degrees of Freedom: 1050 Total (i.e. Null); 1037 Residual
Null Deviance: 742.4
Residual Deviance: 682.8 AIC: 710.8
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.8977 0.2682 0.4043 0.5523 1.1643
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.554e+01 1.023e+03 0.015 0.987886
currentDF[, PROTEIN] 1.573e-01 7.463e-02 2.108 0.035040 *
Age 3.611e-02 1.269e-02 2.846 0.004422 **
Gendermale -4.591e-01 2.387e-01 -1.923 0.054440 .
Hypertension.compositeyes -2.078e-01 3.507e-01 -0.592 0.553537
DiabetesStatusDiabetes 5.715e-02 2.458e-01 0.232 0.816153
SmokerCurrentyes 2.655e-01 2.290e-01 1.159 0.246334
Med.Statin.LLDyes -1.553e-01 2.707e-01 -0.574 0.566039
Med.all.antiplateletyes -9.157e-01 4.483e-01 -2.043 0.041096 *
GFR_MDRD 7.369e-03 5.556e-03 1.326 0.184788
BMI -4.650e-03 2.793e-02 -0.166 0.867781
CAD_history -2.338e-01 2.190e-01 -1.067 0.285776
Stroke_history 1.007e+00 2.711e-01 3.713 0.000205 ***
Peripheral.interv -5.514e-01 2.365e-01 -2.332 0.019724 *
stenose50-70% -1.370e+01 1.023e+03 -0.013 0.989315
stenose70-90% -1.494e+01 1.023e+03 -0.015 0.988351
stenose90-99% -1.472e+01 1.023e+03 -0.014 0.988525
stenose100% (Occlusion) -4.238e-01 1.309e+03 0.000 0.999742
stenose50-99% -1.613e+01 1.023e+03 -0.016 0.987420
stenose70-99% -3.740e-01 1.232e+03 0.000 0.999758
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 742.44 on 1050 degrees of freedom
Residual deviance: 678.70 on 1031 degrees of freedom
AIC: 718.7
Number of Fisher Scoring iterations: 15
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_LN ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_LN
Trait/outcome.............: AsymptSympt
Effect size...............: 0.157312
Standard error............: 0.07463
Odds ratio (effect size)..: 1.17
Lower 95% CI..............: 1.011
Upper 95% CI..............: 1.355
Z-value...................: 2.107901
P-value...................: 0.03503953
Hosmer and Lemeshow r^2...: 0.085846
Cox and Snell r^2.........: 0.058841
Nagelkerke's pseudo r^2...: 0.116151
Sample size of AE DB......: 2388
Sample size of model......: 1051
Missing data %............: 55.98828 cat("Edit the column names...\n")Edit the column names...colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
"Beta", "s.e.m.",
"OR", "low95CI", "up95CI",
"Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")
cat("Correct the variable types...\n")Correct the variable types...GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)
# Save the data
cat("Writing results to Excel-file...\n")Writing results to Excel-file...### Univariate
write.xlsx(GLM.results,
file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Multi.Protein.Symptoms.MODEL2.xlsx"),
row.names = FALSE, col.names = TRUE, sheetName = "Bin.Multi.Symptoms")
# Removing intermediates
cat("Removing intermediate files...\n")Removing intermediate files...rm(TRAIT, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)
Inverse-rank normalization
Here we use the inverse-rank normalized data - visually this is more normally distributed.
GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
PROTEIN = TRAITS.PROTEIN.RANK[protein]
cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
TRAIT = "AsymptSympt"
cat(paste0("\n- processing ",TRAIT,"\n\n"))
currentDF <- as.data.frame(AEDB.CEA %>%
dplyr::select(., PROTEIN, TRAIT, COVARIATES_M2) %>%
filter(complete.cases(.))) %>%
filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
# for debug
# print(DT::datatable(currentDF))
# print(nrow(currentDF))
# print(str(currentDF))
# print(class(currentDF[,TRAIT]))
### univariate
fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose,
data = currentDF, family = binomial(link = "logit"))
model_step <- stepAIC(fit, direction = "both", trace = FALSE)
print(model_step)
print(summary(fit))
GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
GLM.results = rbind(GLM.results, GLM.results.TEMP)
}
Analysis of IL6_rank.
- processing AsymptSympt
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Stroke_history +
Peripheral.interv, family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) Stroke_history Peripheral.interv
1.4460 1.5481 -0.9696
Degrees of Freedom: 480 Total (i.e. Null); 478 Residual
Null Deviance: 448.7
Residual Deviance: 411 AIC: 417
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.8207 0.2472 0.4518 0.6797 1.2683
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.547e+01 1.350e+03 0.011 0.990858
currentDF[, PROTEIN] -1.637e-01 1.341e-01 -1.221 0.222219
Age 2.709e-02 1.676e-02 1.616 0.106099
Gendermale -1.779e-01 2.976e-01 -0.598 0.549922
Hypertension.compositeyes -5.697e-01 4.359e-01 -1.307 0.191211
DiabetesStatusDiabetes 3.403e-01 3.345e-01 1.017 0.308976
SmokerCurrentyes 1.065e-01 2.813e-01 0.379 0.704984
Med.Statin.LLDyes -1.716e-01 3.130e-01 -0.548 0.583524
Med.all.antiplateletyes -6.819e-01 5.289e-01 -1.289 0.197347
GFR_MDRD 1.046e-02 7.628e-03 1.371 0.170375
BMI -8.104e-03 3.455e-02 -0.235 0.814531
CAD_history -8.513e-02 2.809e-01 -0.303 0.761814
Stroke_history 1.578e+00 3.808e-01 4.143 3.42e-05 ***
Peripheral.interv -9.874e-01 2.864e-01 -3.448 0.000564 ***
stenose50-70% -1.393e+01 1.350e+03 -0.010 0.991766
stenose70-90% -1.532e+01 1.350e+03 -0.011 0.990946
stenose90-99% -1.486e+01 1.350e+03 -0.011 0.991218
stenose100% (Occlusion) -1.371e-01 1.610e+03 0.000 0.999932
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 448.65 on 480 degrees of freedom
Residual deviance: 393.24 on 463 degrees of freedom
AIC: 429.24
Number of Fisher Scoring iterations: 15
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_rank ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_rank
Trait/outcome.............: AsymptSympt
Effect size...............: -0.163678
Standard error............: 0.134091
Odds ratio (effect size)..: 0.849
Lower 95% CI..............: 0.653
Upper 95% CI..............: 1.104
Z-value...................: -1.220648
P-value...................: 0.2222193
Hosmer and Lemeshow r^2...: 0.123507
Cox and Snell r^2.........: 0.108813
Nagelkerke's pseudo r^2...: 0.179402
Sample size of AE DB......: 2388
Sample size of model......: 481
Missing data %............: 79.85762
Analysis of MCP1_rank.
- processing AsymptSympt
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Med.all.antiplatelet + Stroke_history + Peripheral.interv,
family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Med.all.antiplateletyes Stroke_history Peripheral.interv
2.1710 0.3020 -0.7129 1.6297 -0.9739
Degrees of Freedom: 513 Total (i.e. Null); 509 Residual
Null Deviance: 457.8
Residual Deviance: 409 AIC: 419
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.8828 0.2333 0.4237 0.6299 1.2824
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.496e+01 1.305e+03 0.011 0.990854
currentDF[, PROTEIN] 3.464e-01 1.367e-01 2.534 0.011287 *
Age 2.580e-02 1.666e-02 1.549 0.121368
Gendermale -2.685e-01 2.997e-01 -0.896 0.370282
Hypertension.compositeyes -5.009e-01 4.399e-01 -1.139 0.254855
DiabetesStatusDiabetes 2.651e-01 3.307e-01 0.802 0.422680
SmokerCurrentyes 7.686e-02 2.804e-01 0.274 0.783994
Med.Statin.LLDyes -4.258e-02 3.194e-01 -0.133 0.893950
Med.all.antiplateletyes -8.355e-01 5.289e-01 -1.580 0.114152
GFR_MDRD 8.463e-03 7.526e-03 1.125 0.260766
BMI 1.930e-02 3.529e-02 0.547 0.584325
CAD_history -2.171e-01 2.814e-01 -0.771 0.440413
Stroke_history 1.598e+00 3.795e-01 4.210 2.55e-05 ***
Peripheral.interv -9.893e-01 2.939e-01 -3.367 0.000761 ***
stenose50-70% -1.353e+01 1.305e+03 -0.010 0.991724
stenose70-90% -1.511e+01 1.305e+03 -0.012 0.990760
stenose90-99% -1.466e+01 1.305e+03 -0.011 0.991034
stenose100% (Occlusion) -1.429e-01 1.629e+03 0.000 0.999930
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 457.77 on 513 degrees of freedom
Residual deviance: 395.26 on 496 degrees of freedom
AIC: 431.26
Number of Fisher Scoring iterations: 15
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: AsymptSympt
Effect size...............: 0.346429
Standard error............: 0.136729
Odds ratio (effect size)..: 1.414
Lower 95% CI..............: 1.082
Upper 95% CI..............: 1.849
Z-value...................: 2.533683
P-value...................: 0.01128706
Hosmer and Lemeshow r^2...: 0.136557
Cox and Snell r^2.........: 0.114514
Nagelkerke's pseudo r^2...: 0.194226
Sample size of AE DB......: 2388
Sample size of model......: 514
Missing data %............: 78.47571
Analysis of IL6_pg_ug_2015_rank.
- processing AsymptSympt
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + Gender +
Med.all.antiplatelet + GFR_MDRD + Stroke_history + Peripheral.interv +
stenose, family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) Age Gendermale Med.all.antiplateletyes GFR_MDRD
15.387606 0.027762 -0.453161 -0.916880 0.008104
Stroke_history Peripheral.interv stenose50-70% stenose70-90% stenose90-99%
1.166727 -0.673292 -13.748714 -14.870639 -14.701591
stenose100% (Occlusion) stenose50-99% stenose70-99%
-0.331218 -16.941956 -0.167408
Degrees of Freedom: 1008 Total (i.e. Null); 996 Residual
Null Deviance: 707.6
Residual Deviance: 652.1 AIC: 678.1
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.8197 0.2654 0.4021 0.5591 0.9812
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.553e+01 1.027e+03 0.015 0.987936
currentDF[, PROTEIN] 6.163e-02 1.053e-01 0.585 0.558336
Age 3.155e-02 1.295e-02 2.436 0.014857 *
Gendermale -4.004e-01 2.443e-01 -1.639 0.101287
Hypertension.compositeyes -2.983e-01 3.648e-01 -0.818 0.413500
DiabetesStatusDiabetes -8.036e-03 2.508e-01 -0.032 0.974442
SmokerCurrentyes 1.551e-01 2.332e-01 0.665 0.505947
Med.Statin.LLDyes -1.778e-01 2.733e-01 -0.651 0.515164
Med.all.antiplateletyes -9.050e-01 4.485e-01 -2.018 0.043614 *
GFR_MDRD 7.277e-03 5.677e-03 1.282 0.199907
BMI 9.863e-04 2.892e-02 0.034 0.972798
CAD_history -1.788e-01 2.262e-01 -0.790 0.429472
Stroke_history 1.108e+00 2.862e-01 3.870 0.000109 ***
Peripheral.interv -6.265e-01 2.400e-01 -2.611 0.009031 **
stenose50-70% -1.368e+01 1.027e+03 -0.013 0.989371
stenose70-90% -1.486e+01 1.027e+03 -0.014 0.988453
stenose90-99% -1.469e+01 1.027e+03 -0.014 0.988582
stenose100% (Occlusion) -4.303e-01 1.306e+03 0.000 0.999737
stenose50-99% -1.691e+01 1.027e+03 -0.016 0.986859
stenose70-99% -1.372e-01 1.233e+03 0.000 0.999911
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 707.63 on 1008 degrees of freedom
Residual deviance: 648.64 on 989 degrees of freedom
AIC: 688.64
Number of Fisher Scoring iterations: 15
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_rank ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_pg_ug_2015_rank
Trait/outcome.............: AsymptSympt
Effect size...............: 0.061634
Standard error............: 0.1053
Odds ratio (effect size)..: 1.064
Lower 95% CI..............: 0.865
Upper 95% CI..............: 1.307
Z-value...................: 0.585315
P-value...................: 0.5583357
Hosmer and Lemeshow r^2...: 0.083364
Cox and Snell r^2.........: 0.056789
Nagelkerke's pseudo r^2...: 0.11266
Sample size of AE DB......: 2388
Sample size of model......: 1009
Missing data %............: 57.74707
Analysis of IL6R_pg_ug_2015_rank.
- processing AsymptSympt
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Med.all.antiplatelet + CAD_history + Stroke_history +
Peripheral.interv, family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Age Gendermale Med.all.antiplateletyes
1.16545 -0.16682 0.02796 -0.35834 -0.82910
CAD_history Stroke_history Peripheral.interv
-0.35351 1.02217 -0.47073
Degrees of Freedom: 1012 Total (i.e. Null); 1005 Residual
Null Deviance: 720.9
Residual Deviance: 677.2 AIC: 693.2
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.8361 0.2711 0.4157 0.5602 1.0121
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.566e+01 1.140e+03 0.014 0.989035
currentDF[, PROTEIN] -1.527e-01 1.016e-01 -1.503 0.132769
Age 3.521e-02 1.291e-02 2.728 0.006368 **
Gendermale -4.077e-01 2.427e-01 -1.680 0.092969 .
Hypertension.compositeyes -2.212e-01 3.506e-01 -0.631 0.528045
DiabetesStatusDiabetes -9.310e-03 2.471e-01 -0.038 0.969943
SmokerCurrentyes 2.704e-01 2.313e-01 1.169 0.242377
Med.Statin.LLDyes -1.877e-01 2.725e-01 -0.689 0.491028
Med.all.antiplateletyes -8.710e-01 4.486e-01 -1.942 0.052179 .
GFR_MDRD 5.154e-03 5.688e-03 0.906 0.364834
BMI -1.404e-02 2.963e-02 -0.474 0.635537
CAD_history -2.680e-01 2.228e-01 -1.203 0.228970
Stroke_history 1.017e+00 2.712e-01 3.751 0.000176 ***
Peripheral.interv -5.189e-01 2.410e-01 -2.153 0.031324 *
stenose50-70% -1.365e+01 1.140e+03 -0.012 0.990444
stenose70-90% -1.482e+01 1.140e+03 -0.013 0.989628
stenose90-99% -1.464e+01 1.140e+03 -0.013 0.989752
stenose100% (Occlusion) -3.777e-01 1.396e+03 0.000 0.999784
stenose50-99% -1.595e+01 1.140e+03 -0.014 0.988836
stenose70-99% -2.526e-01 1.330e+03 0.000 0.999848
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 720.94 on 1012 degrees of freedom
Residual deviance: 662.33 on 993 degrees of freedom
AIC: 702.33
Number of Fisher Scoring iterations: 15
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_rank ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6R_pg_ug_2015_rank
Trait/outcome.............: AsymptSympt
Effect size...............: -0.152701
Standard error............: 0.101579
Odds ratio (effect size)..: 0.858
Lower 95% CI..............: 0.703
Upper 95% CI..............: 1.047
Z-value...................: -1.503271
P-value...................: 0.1327691
Hosmer and Lemeshow r^2...: 0.081298
Cox and Snell r^2.........: 0.056217
Nagelkerke's pseudo r^2...: 0.110406
Sample size of AE DB......: 2388
Sample size of model......: 1013
Missing data %............: 57.57956
Analysis of MCP1_pg_ug_2015_rank.
- processing AsymptSympt
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Med.all.antiplatelet + GFR_MDRD + Stroke_history +
Peripheral.interv + stenose, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Age Gendermale Med.all.antiplateletyes
15.272239 0.246808 0.031733 -0.519803 -0.925714
GFR_MDRD Stroke_history Peripheral.interv stenose50-70% stenose70-90%
0.008448 1.056452 -0.572007 -13.826519 -15.017030
stenose90-99% stenose100% (Occlusion) stenose50-99% stenose70-99%
-14.770785 -0.381146 -16.127189 -0.475975
Degrees of Freedom: 1050 Total (i.e. Null); 1037 Residual
Null Deviance: 742.4
Residual Deviance: 681.8 AIC: 709.8
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.9026 0.2665 0.4041 0.5510 1.1581
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.535e+01 1.022e+03 0.015 0.988014
currentDF[, PROTEIN] 2.327e-01 1.012e-01 2.299 0.021489 *
Age 3.628e-02 1.270e-02 2.857 0.004280 **
Gendermale -4.583e-01 2.387e-01 -1.920 0.054877 .
Hypertension.compositeyes -2.011e-01 3.509e-01 -0.573 0.566720
DiabetesStatusDiabetes 5.331e-02 2.459e-01 0.217 0.828362
SmokerCurrentyes 2.658e-01 2.291e-01 1.160 0.245990
Med.Statin.LLDyes -1.571e-01 2.708e-01 -0.580 0.561715
Med.all.antiplateletyes -9.184e-01 4.485e-01 -2.048 0.040595 *
GFR_MDRD 7.330e-03 5.560e-03 1.318 0.187367
BMI -4.704e-03 2.790e-02 -0.169 0.866132
CAD_history -2.356e-01 2.191e-01 -1.076 0.282037
Stroke_history 1.006e+00 2.713e-01 3.707 0.000209 ***
Peripheral.interv -5.483e-01 2.366e-01 -2.317 0.020485 *
stenose50-70% -1.371e+01 1.022e+03 -0.013 0.989296
stenose70-90% -1.495e+01 1.022e+03 -0.015 0.988326
stenose90-99% -1.473e+01 1.022e+03 -0.014 0.988502
stenose100% (Occlusion) -4.325e-01 1.308e+03 0.000 0.999736
stenose50-99% -1.616e+01 1.022e+03 -0.016 0.987388
stenose70-99% -4.053e-01 1.231e+03 0.000 0.999737
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 742.44 on 1050 degrees of freedom
Residual deviance: 677.73 on 1031 degrees of freedom
AIC: 717.73
Number of Fisher Scoring iterations: 15
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: AsymptSympt
Effect size...............: 0.232718
Standard error............: 0.101213
Odds ratio (effect size)..: 1.262
Lower 95% CI..............: 1.035
Upper 95% CI..............: 1.539
Z-value...................: 2.299283
P-value...................: 0.02148888
Hosmer and Lemeshow r^2...: 0.087159
Cox and Snell r^2.........: 0.059713
Nagelkerke's pseudo r^2...: 0.117873
Sample size of AE DB......: 2388
Sample size of model......: 1051
Missing data %............: 55.98828 cat("Edit the column names...\n")Edit the column names...colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
"Beta", "s.e.m.",
"OR", "low95CI", "up95CI",
"Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")
cat("Correct the variable types...\n")Correct the variable types...GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)
# Save the data
cat("Writing results to Excel-file...\n")Writing results to Excel-file...### Univariate
write.xlsx(GLM.results,
file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Multi.Protein.RANK.Symptoms.MODEL2.xlsx"),
row.names = FALSE, col.names = TRUE, sheetName = "Bin.Multi.Symptoms")
# Removing intermediates
cat("Removing intermediate files...\n")Removing intermediate files...rm(TRAIT, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)
Model 3
In this model we correct for Age, Gender, Hypertension status, Diabetes status, current smoker status, lipid-lowering drugs (LLDs), antiplatelet medication, eGFR (MDRD), BMI, CAD history, stroke history, peripheral interventions, stenosis., and LDL.
Natural log-transformed data
First we use the natural-log transformed data.
GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN)) {
PROTEIN = TRAITS.PROTEIN[protein]
cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
TRAIT = "AsymptSympt"
cat(paste0("\n- processing ",TRAIT,"\n\n"))
currentDF <- as.data.frame(AEDB.CEA %>%
dplyr::select(., PROTEIN, TRAIT, COVARIATES_M3) %>%
filter(complete.cases(.))) %>%
filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
# for debug
# print(DT::datatable(currentDF))
# print(nrow(currentDF))
# print(str(currentDF))
# print(class(currentDF[,TRAIT]))
### univariate
fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose + LDL_final,
data = currentDF, family = binomial(link = "logit"))
model_step <- stepAIC(fit, direction = "both", trace = FALSE)
print(model_step)
print(summary(fit))
GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
GLM.results = rbind(GLM.results, GLM.results.TEMP)
}
Analysis of IL6_LN.
- processing AsymptSympt
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Med.Statin.LLD +
Stroke_history + Peripheral.interv, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) Med.Statin.LLDyes Stroke_history Peripheral.interv
1.8237 -0.6408 1.4565 -1.0636
Degrees of Freedom: 286 Total (i.e. Null); 283 Residual
Null Deviance: 283.3
Residual Deviance: 255.8 AIC: 263.8
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + LDL_final, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.6583 0.2308 0.4365 0.7133 1.3582
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.743e+01 2.400e+03 0.007 0.994206
currentDF[, PROTEIN] -9.886e-02 1.530e-01 -0.646 0.518133
Age -1.025e-02 2.244e-02 -0.457 0.647926
Gendermale 1.919e-01 3.766e-01 0.510 0.610376
Hypertension.compositeyes -9.182e-01 6.105e-01 -1.504 0.132598
DiabetesStatusDiabetes 4.874e-01 4.627e-01 1.053 0.292172
SmokerCurrentyes -2.154e-01 3.505e-01 -0.614 0.538905
Med.Statin.LLDyes -6.461e-01 4.158e-01 -1.554 0.120274
Med.all.antiplateletyes -2.635e-01 7.234e-01 -0.364 0.715643
GFR_MDRD 1.023e-02 9.787e-03 1.045 0.295822
BMI 2.828e-02 4.613e-02 0.613 0.539897
CAD_history 5.117e-02 3.611e-01 0.142 0.887320
Stroke_history 1.660e+00 4.647e-01 3.572 0.000354 ***
Peripheral.interv -1.068e+00 3.563e-01 -2.997 0.002728 **
stenose50-70% -1.521e+01 2.400e+03 -0.006 0.994943
stenose70-90% -1.561e+01 2.400e+03 -0.007 0.994810
stenose90-99% -1.500e+01 2.400e+03 -0.006 0.995013
stenose100% (Occlusion) -6.262e-01 2.644e+03 0.000 0.999811
LDL_final 4.615e-02 1.772e-01 0.260 0.794515
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 283.31 on 286 degrees of freedom
Residual deviance: 245.47 on 268 degrees of freedom
AIC: 283.47
Number of Fisher Scoring iterations: 15
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_LN ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_LN
Trait/outcome.............: AsymptSympt
Effect size...............: -0.09886
Standard error............: 0.15298
Odds ratio (effect size)..: 0.906
Lower 95% CI..............: 0.671
Upper 95% CI..............: 1.223
Z-value...................: -0.646226
P-value...................: 0.518133
Hosmer and Lemeshow r^2...: 0.133551
Cox and Snell r^2.........: 0.123512
Nagelkerke's pseudo r^2...: 0.196878
Sample size of AE DB......: 2388
Sample size of model......: 287
Missing data %............: 87.98158
Analysis of MCP1_LN.
- processing AsymptSympt
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Hypertension.composite +
Med.Statin.LLD + Stroke_history + Peripheral.interv, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) Hypertension.compositeyes Med.Statin.LLDyes Stroke_history Peripheral.interv
2.7182 -0.8944 -0.5833 1.5625 -1.0394
Degrees of Freedom: 356 Total (i.e. Null); 352 Residual
Null Deviance: 326.5
Residual Deviance: 290.8 AIC: 300.8
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + LDL_final, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.8603 0.2234 0.4378 0.6500 1.4250
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.595e+01 2.400e+03 0.007 0.99470
currentDF[, PROTEIN] 1.221e-01 1.860e-01 0.657 0.51131
Age -9.181e-03 2.074e-02 -0.443 0.65799
Gendermale 8.686e-02 3.562e-01 0.244 0.80736
Hypertension.compositeyes -1.162e+00 5.939e-01 -1.956 0.05044 .
DiabetesStatusDiabetes 4.687e-01 4.264e-01 1.099 0.27171
SmokerCurrentyes -1.667e-01 3.273e-01 -0.509 0.61066
Med.Statin.LLDyes -5.187e-01 4.069e-01 -1.275 0.20240
Med.all.antiplateletyes -3.246e-01 7.023e-01 -0.462 0.64397
GFR_MDRD 3.197e-03 9.074e-03 0.352 0.72460
BMI 5.208e-02 4.258e-02 1.223 0.22133
CAD_history 4.633e-02 3.387e-01 0.137 0.89120
Stroke_history 1.776e+00 4.539e-01 3.913 9.11e-05 ***
Peripheral.interv -1.059e+00 3.409e-01 -3.107 0.00189 **
stenose50-70% -1.395e+01 2.400e+03 -0.006 0.99536
stenose70-90% -1.508e+01 2.400e+03 -0.006 0.99499
stenose90-99% -1.439e+01 2.400e+03 -0.006 0.99522
stenose100% (Occlusion) -1.415e-01 2.638e+03 0.000 0.99996
LDL_final 7.818e-02 1.579e-01 0.495 0.62044
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 326.48 on 356 degrees of freedom
Residual deviance: 280.97 on 338 degrees of freedom
AIC: 318.97
Number of Fisher Scoring iterations: 15
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_LN ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_LN
Trait/outcome.............: AsymptSympt
Effect size...............: 0.122134
Standard error............: 0.185953
Odds ratio (effect size)..: 1.13
Lower 95% CI..............: 0.785
Upper 95% CI..............: 1.627
Z-value...................: 0.656804
P-value...................: 0.5113069
Hosmer and Lemeshow r^2...: 0.139416
Cox and Snell r^2.........: 0.119706
Nagelkerke's pseudo r^2...: 0.199745
Sample size of AE DB......: 2388
Sample size of model......: 357
Missing data %............: 85.05025
Analysis of IL6_pg_ug_2015_LN.
- processing AsymptSympt
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Med.Statin.LLD +
Med.all.antiplatelet + Stroke_history, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) Med.Statin.LLDyes Med.all.antiplateletyes Stroke_history
3.1771 -0.6599 -0.9416 1.5973
Degrees of Freedom: 627 Total (i.e. Null); 624 Residual
Null Deviance: 430.7
Residual Deviance: 402.5 AIC: 410.5
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + LDL_final, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.9013 0.2226 0.3631 0.5804 1.0099
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.624e+01 1.229e+03 0.013 0.989456
currentDF[, PROTEIN] 6.654e-02 9.000e-02 0.739 0.459706
Age 2.066e-02 1.681e-02 1.229 0.218929
Gendermale -2.292e-01 3.088e-01 -0.742 0.457880
Hypertension.compositeyes -4.168e-01 5.055e-01 -0.825 0.409591
DiabetesStatusDiabetes 1.173e-01 3.342e-01 0.351 0.725677
SmokerCurrentyes 1.632e-01 2.972e-01 0.549 0.582939
Med.Statin.LLDyes -5.393e-01 3.809e-01 -1.416 0.156827
Med.all.antiplateletyes -8.736e-01 6.272e-01 -1.393 0.163638
GFR_MDRD 9.608e-03 7.419e-03 1.295 0.195315
BMI -9.234e-03 3.595e-02 -0.257 0.797294
CAD_history 1.169e-01 2.991e-01 0.391 0.695979
Stroke_history 1.520e+00 4.175e-01 3.641 0.000272 ***
Peripheral.interv -3.896e-01 3.152e-01 -1.236 0.216371
stenose50-70% -1.345e+01 1.229e+03 -0.011 0.991264
stenose70-90% -1.472e+01 1.229e+03 -0.012 0.990444
stenose90-99% -1.480e+01 1.229e+03 -0.012 0.990388
stenose100% (Occlusion) -3.152e-01 1.488e+03 0.000 0.999831
stenose70-99% -6.828e-01 1.680e+03 0.000 0.999676
LDL_final 1.413e-01 1.428e-01 0.990 0.322215
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 430.69 on 627 degrees of freedom
Residual deviance: 389.70 on 608 degrees of freedom
AIC: 429.7
Number of Fisher Scoring iterations: 15
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_LN ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_pg_ug_2015_LN
Trait/outcome.............: AsymptSympt
Effect size...............: 0.066541
Standard error............: 0.090002
Odds ratio (effect size)..: 1.069
Lower 95% CI..............: 0.896
Upper 95% CI..............: 1.275
Z-value...................: 0.739331
P-value...................: 0.4597058
Hosmer and Lemeshow r^2...: 0.095178
Cox and Snell r^2.........: 0.063189
Nagelkerke's pseudo r^2...: 0.127316
Sample size of AE DB......: 2388
Sample size of model......: 628
Missing data %............: 73.70184
Analysis of IL6R_pg_ug_2015_LN.
- processing AsymptSympt
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Med.Statin.LLD + Med.all.antiplatelet + Stroke_history, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Med.Statin.LLDyes Med.all.antiplateletyes Stroke_history
2.7049 -0.2566 -0.7107 -0.9239 1.5983
Degrees of Freedom: 621 Total (i.e. Null); 617 Residual
Null Deviance: 437.6
Residual Deviance: 404.5 AIC: 414.5
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + LDL_final, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.8997 0.2272 0.3710 0.5857 1.0333
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.636e+01 1.460e+03 0.011 0.991063
currentDF[, PROTEIN] -2.183e-01 1.310e-01 -1.667 0.095557 .
Age 2.062e-02 1.696e-02 1.216 0.223987
Gendermale -2.038e-01 3.036e-01 -0.671 0.502095
Hypertension.compositeyes -2.825e-01 4.701e-01 -0.601 0.547898
DiabetesStatusDiabetes 1.002e-01 3.345e-01 0.300 0.764523
SmokerCurrentyes 2.855e-01 2.958e-01 0.965 0.334510
Med.Statin.LLDyes -5.857e-01 3.858e-01 -1.518 0.128986
Med.all.antiplateletyes -8.778e-01 6.298e-01 -1.394 0.163379
GFR_MDRD 7.729e-03 7.508e-03 1.029 0.303282
BMI -2.886e-02 3.764e-02 -0.767 0.443288
CAD_history 3.983e-02 2.975e-01 0.134 0.893487
Stroke_history 1.563e+00 4.159e-01 3.757 0.000172 ***
Peripheral.interv -3.196e-01 3.172e-01 -1.007 0.313790
stenose50-70% -1.362e+01 1.460e+03 -0.009 0.992555
stenose70-90% -1.487e+01 1.460e+03 -0.010 0.991872
stenose90-99% -1.495e+01 1.460e+03 -0.010 0.991831
stenose100% (Occlusion) -5.480e-01 1.681e+03 0.000 0.999740
stenose70-99% -8.238e-01 1.824e+03 0.000 0.999640
LDL_final 1.095e-01 1.432e-01 0.765 0.444488
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 437.63 on 621 degrees of freedom
Residual deviance: 393.77 on 602 degrees of freedom
AIC: 433.77
Number of Fisher Scoring iterations: 15
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_LN ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6R_pg_ug_2015_LN
Trait/outcome.............: AsymptSympt
Effect size...............: -0.218277
Standard error............: 0.130957
Odds ratio (effect size)..: 0.804
Lower 95% CI..............: 0.622
Upper 95% CI..............: 1.039
Z-value...................: -1.666785
P-value...................: 0.09555717
Hosmer and Lemeshow r^2...: 0.100216
Cox and Snell r^2.........: 0.068082
Nagelkerke's pseudo r^2...: 0.134765
Sample size of AE DB......: 2388
Sample size of model......: 622
Missing data %............: 73.9531
Analysis of MCP1_pg_ug_2015_LN.
- processing AsymptSympt
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Med.Statin.LLD +
Med.all.antiplatelet + Stroke_history, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) Med.Statin.LLDyes Med.all.antiplateletyes Stroke_history
3.2384 -0.6894 -0.9942 1.6182
Degrees of Freedom: 643 Total (i.e. Null); 640 Residual
Null Deviance: 447
Residual Deviance: 416.9 AIC: 424.9
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + LDL_final, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-3.0012 0.2287 0.3669 0.5798 1.0121
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.613e+01 1.224e+03 0.013 0.989483
currentDF[, PROTEIN] 8.739e-02 9.435e-02 0.926 0.354313
Age 2.107e-02 1.664e-02 1.266 0.205401
Gendermale -2.293e-01 3.001e-01 -0.764 0.444929
Hypertension.compositeyes -2.522e-01 4.705e-01 -0.536 0.591933
DiabetesStatusDiabetes 1.980e-01 3.319e-01 0.597 0.550823
SmokerCurrentyes 2.748e-01 2.931e-01 0.938 0.348465
Med.Statin.LLDyes -5.582e-01 3.798e-01 -1.470 0.141601
Med.all.antiplateletyes -9.239e-01 6.261e-01 -1.476 0.140042
GFR_MDRD 1.026e-02 7.344e-03 1.398 0.162163
BMI -1.469e-02 3.490e-02 -0.421 0.673791
CAD_history 3.673e-02 2.893e-01 0.127 0.898982
Stroke_history 1.540e+00 4.161e-01 3.702 0.000214 ***
Peripheral.interv -3.612e-01 3.123e-01 -1.157 0.247446
stenose50-70% -1.341e+01 1.224e+03 -0.011 0.991254
stenose70-90% -1.472e+01 1.224e+03 -0.012 0.990403
stenose90-99% -1.478e+01 1.224e+03 -0.012 0.990363
stenose100% (Occlusion) -3.058e-01 1.487e+03 0.000 0.999836
stenose70-99% -7.269e-01 1.674e+03 0.000 0.999654
LDL_final 1.158e-01 1.394e-01 0.831 0.406028
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 446.98 on 643 degrees of freedom
Residual deviance: 403.91 on 624 degrees of freedom
AIC: 443.91
Number of Fisher Scoring iterations: 15
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_LN ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_LN
Trait/outcome.............: AsymptSympt
Effect size...............: 0.08739
Standard error............: 0.094348
Odds ratio (effect size)..: 1.091
Lower 95% CI..............: 0.907
Upper 95% CI..............: 1.313
Z-value...................: 0.926256
P-value...................: 0.3543129
Hosmer and Lemeshow r^2...: 0.096355
Cox and Snell r^2.........: 0.06469
Nagelkerke's pseudo r^2...: 0.129261
Sample size of AE DB......: 2388
Sample size of model......: 644
Missing data %............: 73.03183 cat("Edit the column names...\n")Edit the column names...colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
"Beta", "s.e.m.",
"OR", "low95CI", "up95CI",
"Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")
cat("Correct the variable types...\n")Correct the variable types...GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)
# Save the data
cat("Writing results to Excel-file...\n")Writing results to Excel-file...### Univariate
write.xlsx(GLM.results,
file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Multi.Protein.Symptoms.MODEL3.xlsx"),
row.names = FALSE, col.names = TRUE, sheetName = "Bin.Multi.Symptoms")
# Removing intermediates
cat("Removing intermediate files...\n")Removing intermediate files...rm(TRAIT, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)
Inverse-rank normalization
Here we use the inverse-rank normalized data - visually this is more normally distributed.
GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
PROTEIN = TRAITS.PROTEIN.RANK[protein]
cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
TRAIT = "AsymptSympt"
cat(paste0("\n- processing ",TRAIT,"\n\n"))
currentDF <- as.data.frame(AEDB.CEA %>%
dplyr::select(., PROTEIN, TRAIT, COVARIATES_M3) %>%
filter(complete.cases(.))) %>%
filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
# for debug
# print(DT::datatable(currentDF))
# print(nrow(currentDF))
# print(str(currentDF))
# print(class(currentDF[,TRAIT]))
### univariate
fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose + LDL_final,
data = currentDF, family = binomial(link = "logit"))
model_step <- stepAIC(fit, direction = "both", trace = FALSE)
print(model_step)
print(summary(fit))
GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
GLM.results = rbind(GLM.results, GLM.results.TEMP)
}
Analysis of IL6_rank.
- processing AsymptSympt
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Hypertension.composite +
Med.Statin.LLD + Stroke_history + Peripheral.interv, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) Hypertension.compositeyes Med.Statin.LLDyes Stroke_history Peripheral.interv
2.3019 -0.6834 -0.4871 1.5512 -0.8887
Degrees of Freedom: 340 Total (i.e. Null); 336 Residual
Null Deviance: 332.2
Residual Deviance: 300.6 AIC: 310.6
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + LDL_final, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.7064 0.2184 0.4642 0.6939 1.3335
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.503e+01 2.400e+03 0.006 0.995001
currentDF[, PROTEIN] -1.678e-01 1.551e-01 -1.082 0.279290
Age 8.507e-03 1.982e-02 0.429 0.667733
Gendermale -1.123e-02 3.495e-01 -0.032 0.974361
Hypertension.compositeyes -9.387e-01 5.484e-01 -1.712 0.086960 .
DiabetesStatusDiabetes 2.877e-01 4.062e-01 0.708 0.478734
SmokerCurrentyes 6.318e-02 3.245e-01 0.195 0.845601
Med.Statin.LLDyes -3.893e-01 3.852e-01 -1.011 0.312188
Med.all.antiplateletyes -4.096e-01 7.039e-01 -0.582 0.560617
GFR_MDRD 9.846e-03 9.026e-03 1.091 0.275351
BMI 4.511e-02 4.280e-02 1.054 0.291860
CAD_history 1.774e-01 3.336e-01 0.532 0.594950
Stroke_history 1.716e+00 4.478e-01 3.833 0.000127 ***
Peripheral.interv -9.306e-01 3.312e-01 -2.809 0.004964 **
stenose50-70% -1.474e+01 2.400e+03 -0.006 0.995098
stenose70-90% -1.563e+01 2.400e+03 -0.007 0.994803
stenose90-99% -1.491e+01 2.400e+03 -0.006 0.995042
stenose100% (Occlusion) -4.936e-01 2.598e+03 0.000 0.999848
LDL_final 1.420e-01 1.596e-01 0.890 0.373659
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 332.21 on 340 degrees of freedom
Residual deviance: 288.59 on 322 degrees of freedom
AIC: 326.59
Number of Fisher Scoring iterations: 15
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_rank ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_rank
Trait/outcome.............: AsymptSympt
Effect size...............: -0.16776
Standard error............: 0.155058
Odds ratio (effect size)..: 0.846
Lower 95% CI..............: 0.624
Upper 95% CI..............: 1.146
Z-value...................: -1.081915
P-value...................: 0.2792904
Hosmer and Lemeshow r^2...: 0.131299
Cox and Snell r^2.........: 0.120072
Nagelkerke's pseudo r^2...: 0.192882
Sample size of AE DB......: 2388
Sample size of model......: 341
Missing data %............: 85.72027
Analysis of MCP1_rank.
- processing AsymptSympt
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Med.Statin.LLD + Stroke_history + Peripheral.interv, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Med.Statin.LLDyes Stroke_history Peripheral.interv
1.8016 0.2201 -0.5266 1.5858 -0.9498
Degrees of Freedom: 361 Total (i.e. Null); 357 Residual
Null Deviance: 337.7
Residual Deviance: 302.5 AIC: 312.5
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + LDL_final, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.9148 0.2219 0.4302 0.6571 1.4101
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.492e+01 2.400e+03 0.006 0.9950
currentDF[, PROTEIN] 2.476e-01 1.571e-01 1.576 0.1149
Age 5.395e-03 1.958e-02 0.276 0.7829
Gendermale 2.885e-03 3.485e-01 0.008 0.9934
Hypertension.compositeyes -8.618e-01 5.437e-01 -1.585 0.1130
DiabetesStatusDiabetes 2.990e-01 4.035e-01 0.741 0.4587
SmokerCurrentyes -1.908e-02 3.211e-01 -0.059 0.9526
Med.Statin.LLDyes -3.412e-01 3.877e-01 -0.880 0.3788
Med.all.antiplateletyes -6.108e-01 6.969e-01 -0.876 0.3808
GFR_MDRD 4.538e-03 8.951e-03 0.507 0.6122
BMI 5.397e-02 4.123e-02 1.309 0.1906
CAD_history 7.558e-02 3.358e-01 0.225 0.8219
Stroke_history 1.738e+00 4.468e-01 3.890 0.0001 ***
Peripheral.interv -9.657e-01 3.364e-01 -2.870 0.0041 **
stenose50-70% -1.369e+01 2.400e+03 -0.006 0.9954
stenose70-90% -1.483e+01 2.400e+03 -0.006 0.9951
stenose90-99% -1.423e+01 2.400e+03 -0.006 0.9953
stenose100% (Occlusion) 5.240e-02 2.639e+03 0.000 1.0000
LDL_final 1.165e-01 1.571e-01 0.742 0.4584
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 337.75 on 361 degrees of freedom
Residual deviance: 291.85 on 343 degrees of freedom
AIC: 329.85
Number of Fisher Scoring iterations: 15
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: AsymptSympt
Effect size...............: 0.24763
Standard error............: 0.157079
Odds ratio (effect size)..: 1.281
Lower 95% CI..............: 0.942
Upper 95% CI..............: 1.743
Z-value...................: 1.576468
P-value...................: 0.1149179
Hosmer and Lemeshow r^2...: 0.135895
Cox and Snell r^2.........: 0.119082
Nagelkerke's pseudo r^2...: 0.196301
Sample size of AE DB......: 2388
Sample size of model......: 362
Missing data %............: 84.84087
Analysis of IL6_pg_ug_2015_rank.
- processing AsymptSympt
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Med.Statin.LLD +
Med.all.antiplatelet + Stroke_history, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) Med.Statin.LLDyes Med.all.antiplateletyes Stroke_history
3.1771 -0.6599 -0.9416 1.5973
Degrees of Freedom: 627 Total (i.e. Null); 624 Residual
Null Deviance: 430.7
Residual Deviance: 402.5 AIC: 410.5
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + LDL_final, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.8925 0.2219 0.3628 0.5798 1.0169
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.601e+01 1.228e+03 0.013 0.989598
currentDF[, PROTEIN] 1.091e-01 1.344e-01 0.812 0.416921
Age 2.057e-02 1.680e-02 1.224 0.220796
Gendermale -2.285e-01 3.088e-01 -0.740 0.459267
Hypertension.compositeyes -4.162e-01 5.055e-01 -0.823 0.410251
DiabetesStatusDiabetes 1.200e-01 3.342e-01 0.359 0.719686
SmokerCurrentyes 1.622e-01 2.972e-01 0.546 0.585130
Med.Statin.LLDyes -5.403e-01 3.809e-01 -1.418 0.156084
Med.all.antiplateletyes -8.754e-01 6.273e-01 -1.395 0.162898
GFR_MDRD 9.640e-03 7.422e-03 1.299 0.193998
BMI -8.759e-03 3.597e-02 -0.244 0.807588
CAD_history 1.165e-01 2.990e-01 0.390 0.696832
Stroke_history 1.518e+00 4.174e-01 3.637 0.000275 ***
Peripheral.interv -3.876e-01 3.153e-01 -1.230 0.218860
stenose50-70% -1.345e+01 1.228e+03 -0.011 0.991262
stenose70-90% -1.472e+01 1.228e+03 -0.012 0.990440
stenose90-99% -1.480e+01 1.228e+03 -0.012 0.990386
stenose100% (Occlusion) -3.087e-01 1.487e+03 0.000 0.999834
stenose70-99% -6.794e-01 1.680e+03 0.000 0.999677
LDL_final 1.423e-01 1.428e-01 0.996 0.319054
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 430.69 on 627 degrees of freedom
Residual deviance: 389.58 on 608 degrees of freedom
AIC: 429.58
Number of Fisher Scoring iterations: 15
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_rank ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_pg_ug_2015_rank
Trait/outcome.............: AsymptSympt
Effect size...............: 0.10907
Standard error............: 0.13436
Odds ratio (effect size)..: 1.115
Lower 95% CI..............: 0.857
Upper 95% CI..............: 1.451
Z-value...................: 0.811775
P-value...................: 0.4169206
Hosmer and Lemeshow r^2...: 0.095441
Cox and Snell r^2.........: 0.063358
Nagelkerke's pseudo r^2...: 0.127657
Sample size of AE DB......: 2388
Sample size of model......: 628
Missing data %............: 73.70184
Analysis of IL6R_pg_ug_2015_rank.
- processing AsymptSympt
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Med.Statin.LLD + Med.all.antiplatelet + Stroke_history, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Med.Statin.LLDyes Med.all.antiplateletyes Stroke_history
3.2331 -0.2533 -0.7112 -0.9302 1.5950
Degrees of Freedom: 621 Total (i.e. Null); 617 Residual
Null Deviance: 437.6
Residual Deviance: 405 AIC: 415
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + LDL_final, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.8977 0.2263 0.3772 0.5824 1.0301
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.675e+01 1.461e+03 0.011 0.990849
currentDF[, PROTEIN] -2.135e-01 1.341e-01 -1.591 0.111538
Age 2.067e-02 1.695e-02 1.220 0.222595
Gendermale -1.975e-01 3.036e-01 -0.651 0.515298
Hypertension.compositeyes -2.786e-01 4.702e-01 -0.593 0.553454
DiabetesStatusDiabetes 1.106e-01 3.342e-01 0.331 0.740614
SmokerCurrentyes 2.920e-01 2.957e-01 0.988 0.323314
Med.Statin.LLDyes -5.915e-01 3.857e-01 -1.533 0.125200
Med.all.antiplateletyes -8.789e-01 6.296e-01 -1.396 0.162715
GFR_MDRD 7.783e-03 7.510e-03 1.036 0.300033
BMI -2.873e-02 3.768e-02 -0.762 0.445850
CAD_history 4.751e-02 2.974e-01 0.160 0.873088
Stroke_history 1.559e+00 4.159e-01 3.748 0.000178 ***
Peripheral.interv -3.233e-01 3.174e-01 -1.018 0.308453
stenose50-70% -1.358e+01 1.461e+03 -0.009 0.992580
stenose70-90% -1.484e+01 1.461e+03 -0.010 0.991893
stenose90-99% -1.492e+01 1.461e+03 -0.010 0.991852
stenose100% (Occlusion) -5.222e-01 1.682e+03 0.000 0.999752
stenose70-99% -7.740e-01 1.825e+03 0.000 0.999662
LDL_final 1.077e-01 1.432e-01 0.752 0.452007
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 437.63 on 621 degrees of freedom
Residual deviance: 394.25 on 602 degrees of freedom
AIC: 434.25
Number of Fisher Scoring iterations: 15
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_rank ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6R_pg_ug_2015_rank
Trait/outcome.............: AsymptSympt
Effect size...............: -0.213474
Standard error............: 0.134149
Odds ratio (effect size)..: 0.808
Lower 95% CI..............: 0.621
Upper 95% CI..............: 1.051
Z-value...................: -1.59132
P-value...................: 0.1115376
Hosmer and Lemeshow r^2...: 0.099128
Cox and Snell r^2.........: 0.067368
Nagelkerke's pseudo r^2...: 0.133352
Sample size of AE DB......: 2388
Sample size of model......: 622
Missing data %............: 73.9531
Analysis of MCP1_pg_ug_2015_rank.
- processing AsymptSympt
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Med.Statin.LLD +
Med.all.antiplatelet + Stroke_history, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) Med.Statin.LLDyes Med.all.antiplateletyes Stroke_history
3.2384 -0.6894 -0.9942 1.6182
Degrees of Freedom: 643 Total (i.e. Null); 640 Residual
Null Deviance: 447
Residual Deviance: 416.9 AIC: 424.9
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + LDL_final, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-3.0043 0.2305 0.3632 0.5759 1.0192
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.601e+01 1.223e+03 0.013 0.98956
currentDF[, PROTEIN] 1.380e-01 1.256e-01 1.099 0.27189
Age 2.121e-02 1.665e-02 1.274 0.20279
Gendermale -2.311e-01 3.001e-01 -0.770 0.44137
Hypertension.compositeyes -2.425e-01 4.705e-01 -0.515 0.60630
DiabetesStatusDiabetes 1.947e-01 3.318e-01 0.587 0.55735
SmokerCurrentyes 2.765e-01 2.932e-01 0.943 0.34562
Med.Statin.LLDyes -5.572e-01 3.798e-01 -1.467 0.14235
Med.all.antiplateletyes -9.251e-01 6.263e-01 -1.477 0.13967
GFR_MDRD 1.031e-02 7.351e-03 1.402 0.16085
BMI -1.427e-02 3.488e-02 -0.409 0.68230
CAD_history 3.312e-02 2.894e-01 0.114 0.90887
Stroke_history 1.537e+00 4.161e-01 3.695 0.00022 ***
Peripheral.interv -3.618e-01 3.123e-01 -1.158 0.24670
stenose50-70% -1.342e+01 1.223e+03 -0.011 0.99124
stenose70-90% -1.472e+01 1.223e+03 -0.012 0.99039
stenose90-99% -1.479e+01 1.223e+03 -0.012 0.99035
stenose100% (Occlusion) -3.050e-01 1.487e+03 0.000 0.99984
stenose70-99% -7.617e-01 1.676e+03 0.000 0.99964
LDL_final 1.144e-01 1.395e-01 0.821 0.41190
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 446.98 on 643 degrees of freedom
Residual deviance: 403.54 on 624 degrees of freedom
AIC: 443.54
Number of Fisher Scoring iterations: 15
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: AsymptSympt
Effect size...............: 0.138042
Standard error............: 0.12564
Odds ratio (effect size)..: 1.148
Lower 95% CI..............: 0.897
Upper 95% CI..............: 1.469
Z-value...................: 1.098713
P-value...................: 0.2718933
Hosmer and Lemeshow r^2...: 0.097176
Cox and Snell r^2.........: 0.065223
Nagelkerke's pseudo r^2...: 0.130325
Sample size of AE DB......: 2388
Sample size of model......: 644
Missing data %............: 73.03183 cat("Edit the column names...\n")Edit the column names...colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
"Beta", "s.e.m.",
"OR", "low95CI", "up95CI",
"Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")
cat("Correct the variable types...\n")Correct the variable types...GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)
# Save the data
cat("Writing results to Excel-file...\n")Writing results to Excel-file...### Univariate
write.xlsx(GLM.results,
file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Multi.Protein.RANK.Symptoms.MODEL3.xlsx"),
row.names = FALSE, col.names = TRUE, sheetName = "Bin.Multi.Symptoms")
# Removing intermediates
cat("Removing intermediate files...\n")Removing intermediate files...rm(TRAIT, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)
Model 4
In this model we correct for Age, Gender, Hypertension status, Diabetes status, current smoker status, lipid-lowering drugs (LLDs), antiplatelet medication, eGFR (MDRD), BMI, CAD history, stroke history, peripheral interventions, stenosis., and hsCRP.
Natural log-transformed data
First we use the natural-log transformed data.
GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN)) {
PROTEIN = TRAITS.PROTEIN[protein]
cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
TRAIT = "AsymptSympt"
cat(paste0("\n- processing ",TRAIT,"\n\n"))
currentDF <- as.data.frame(AEDB.CEA %>%
dplyr::select(., PROTEIN, TRAIT, COVARIATES_M4) %>%
filter(complete.cases(.))) %>%
filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
# for debug
# print(DT::datatable(currentDF))
# print(nrow(currentDF))
# print(str(currentDF))
# print(class(currentDF[,TRAIT]))
### univariate
fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose + hsCRP_plasma,
data = currentDF, family = binomial(link = "logit"))
model_step <- stepAIC(fit, direction = "both", trace = FALSE)
print(model_step)
print(summary(fit))
GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
GLM.results = rbind(GLM.results, GLM.results.TEMP)
}
Analysis of IL6_LN.
- processing AsymptSympt
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Hypertension.composite +
DiabetesStatus + Med.Statin.LLD + Stroke_history + Peripheral.interv,
family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) Hypertension.compositeyes DiabetesStatusDiabetes Med.Statin.LLDyes Stroke_history
2.5245 -0.7846 0.7607 -0.9893 1.2704
Peripheral.interv
-0.8749
Degrees of Freedom: 227 Total (i.e. Null); 222 Residual
Null Deviance: 237.3
Residual Deviance: 212.7 AIC: 224.7
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + hsCRP_plasma, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.2733 0.1875 0.4731 0.7314 1.3597
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 2.050e+01 2.400e+03 0.009 0.99318
currentDF[, PROTEIN] -1.933e-01 1.787e-01 -1.081 0.27953
Age -2.313e-02 2.555e-02 -0.906 0.36513
Gendermale 3.848e-01 4.022e-01 0.957 0.33877
Hypertension.compositeyes -1.147e+00 6.318e-01 -1.816 0.06937 .
DiabetesStatusDiabetes 8.272e-01 5.544e-01 1.492 0.13573
SmokerCurrentyes -1.491e-01 3.911e-01 -0.381 0.70302
Med.Statin.LLDyes -1.042e+00 4.415e-01 -2.360 0.01826 *
Med.all.antiplateletyes -6.377e-02 7.479e-01 -0.085 0.93206
GFR_MDRD 7.121e-03 1.166e-02 0.611 0.54139
BMI -2.683e-03 4.738e-02 -0.057 0.95484
CAD_history 1.288e-01 3.988e-01 0.323 0.74675
Stroke_history 1.360e+00 4.785e-01 2.842 0.00448 **
Peripheral.interv -8.959e-01 3.926e-01 -2.282 0.02248 *
stenose50-70% -1.757e+01 2.400e+03 -0.007 0.99416
stenose70-90% -1.633e+01 2.400e+03 -0.007 0.99457
stenose90-99% -1.576e+01 2.400e+03 -0.007 0.99476
stenose100% (Occlusion) -1.483e+00 2.703e+03 -0.001 0.99956
hsCRP_plasma 3.120e-03 5.019e-03 0.622 0.53423
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 237.30 on 227 degrees of freedom
Residual deviance: 204.33 on 209 degrees of freedom
AIC: 242.33
Number of Fisher Scoring iterations: 15
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_LN ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_LN
Trait/outcome.............: AsymptSympt
Effect size...............: -0.193256
Standard error............: 0.178712
Odds ratio (effect size)..: 0.824
Lower 95% CI..............: 0.581
Upper 95% CI..............: 1.17
Z-value...................: -1.081383
P-value...................: 0.2795267
Hosmer and Lemeshow r^2...: 0.138941
Cox and Snell r^2.........: 0.134639
Nagelkerke's pseudo r^2...: 0.208154
Sample size of AE DB......: 2388
Sample size of model......: 228
Missing data %............: 90.45226
Analysis of MCP1_LN.
- processing AsymptSympt
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Hypertension.composite +
DiabetesStatus + Med.Statin.LLD + Stroke_history + Peripheral.interv,
family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) Hypertension.compositeyes DiabetesStatusDiabetes Med.Statin.LLDyes Stroke_history
2.7455 -0.9488 0.7094 -0.9165 1.3830
Peripheral.interv
-0.7997
Degrees of Freedom: 278 Total (i.e. Null); 273 Residual
Null Deviance: 271.3
Residual Deviance: 242.8 AIC: 254.8
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + hsCRP_plasma, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.5328 0.2397 0.4550 0.6805 1.4332
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.853e+01 2.400e+03 0.008 0.99384
currentDF[, PROTEIN] 1.639e-01 2.012e-01 0.815 0.41522
Age -2.290e-02 2.336e-02 -0.980 0.32696
Gendermale 1.420e-01 3.780e-01 0.376 0.70722
Hypertension.compositeyes -1.187e+00 6.013e-01 -1.975 0.04828 *
DiabetesStatusDiabetes 7.247e-01 4.877e-01 1.486 0.13728
SmokerCurrentyes -1.354e-01 3.633e-01 -0.373 0.70942
Med.Statin.LLDyes -9.441e-01 4.386e-01 -2.152 0.03136 *
Med.all.antiplateletyes -2.041e-01 7.140e-01 -0.286 0.77496
GFR_MDRD -2.775e-03 1.030e-02 -0.269 0.78761
BMI 1.860e-02 4.290e-02 0.434 0.66456
CAD_history 1.981e-01 3.695e-01 0.536 0.59196
Stroke_history 1.529e+00 4.694e-01 3.258 0.00112 **
Peripheral.interv -8.343e-01 3.707e-01 -2.251 0.02439 *
stenose50-70% -1.531e+01 2.400e+03 -0.006 0.99491
stenose70-90% -1.543e+01 2.400e+03 -0.006 0.99487
stenose90-99% -1.481e+01 2.400e+03 -0.006 0.99508
stenose100% (Occlusion) -3.712e-01 2.690e+03 0.000 0.99989
hsCRP_plasma 1.275e-03 3.465e-03 0.368 0.71299
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 271.28 on 278 degrees of freedom
Residual deviance: 236.26 on 260 degrees of freedom
AIC: 274.26
Number of Fisher Scoring iterations: 15
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_LN ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_LN
Trait/outcome.............: AsymptSympt
Effect size...............: 0.163894
Standard error............: 0.201161
Odds ratio (effect size)..: 1.178
Lower 95% CI..............: 0.794
Upper 95% CI..............: 1.747
Z-value...................: 0.814742
P-value...................: 0.4152199
Hosmer and Lemeshow r^2...: 0.129113
Cox and Snell r^2.........: 0.117981
Nagelkerke's pseudo r^2...: 0.18974
Sample size of AE DB......: 2388
Sample size of model......: 279
Missing data %............: 88.31658
Analysis of IL6_pg_ug_2015_LN.
- processing AsymptSympt
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + CAD_history + Stroke_history +
Peripheral.interv, family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) SmokerCurrentyes Med.Statin.LLDyes Med.all.antiplateletyes CAD_history
3.5223 0.4197 -0.7448 -1.3388 0.4457
Stroke_history Peripheral.interv
1.0675 -0.6632
Degrees of Freedom: 618 Total (i.e. Null); 612 Residual
Null Deviance: 449.1
Residual Deviance: 418 AIC: 432
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + hsCRP_plasma, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-3.0434 0.2574 0.4128 0.5582 1.0607
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.681e+01 1.296e+03 0.013 0.98965
currentDF[, PROTEIN] 4.923e-02 9.140e-02 0.539 0.59013
Age 2.097e-02 1.651e-02 1.270 0.20406
Gendermale -1.581e-01 2.913e-01 -0.543 0.58724
Hypertension.compositeyes -2.427e-01 4.429e-01 -0.548 0.58375
DiabetesStatusDiabetes 1.106e-01 3.301e-01 0.335 0.73760
SmokerCurrentyes 4.600e-01 3.004e-01 1.532 0.12562
Med.Statin.LLDyes -6.978e-01 3.703e-01 -1.885 0.05948 .
Med.all.antiplateletyes -1.297e+00 7.501e-01 -1.729 0.08374 .
GFR_MDRD 7.343e-03 7.465e-03 0.984 0.32531
BMI -1.178e-02 3.354e-02 -0.351 0.72539
CAD_history 4.664e-01 3.078e-01 1.516 0.12962
Stroke_history 9.369e-01 3.522e-01 2.660 0.00781 **
Peripheral.interv -6.347e-01 2.947e-01 -2.153 0.03129 *
stenose50-70% -1.382e+01 1.296e+03 -0.011 0.99149
stenose70-90% -1.461e+01 1.296e+03 -0.011 0.99100
stenose90-99% -1.459e+01 1.296e+03 -0.011 0.99101
stenose100% (Occlusion) -8.537e-01 1.729e+03 0.000 0.99961
stenose70-99% -2.552e-01 1.603e+03 0.000 0.99987
hsCRP_plasma 2.006e-03 4.527e-03 0.443 0.65764
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 449.13 on 618 degrees of freedom
Residual deviance: 410.28 on 599 degrees of freedom
AIC: 450.28
Number of Fisher Scoring iterations: 15
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_LN ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_pg_ug_2015_LN
Trait/outcome.............: AsymptSympt
Effect size...............: 0.049231
Standard error............: 0.091399
Odds ratio (effect size)..: 1.05
Lower 95% CI..............: 0.878
Upper 95% CI..............: 1.257
Z-value...................: 0.538646
P-value...................: 0.5901313
Hosmer and Lemeshow r^2...: 0.086503
Cox and Snell r^2.........: 0.060835
Nagelkerke's pseudo r^2...: 0.117908
Sample size of AE DB......: 2388
Sample size of model......: 619
Missing data %............: 74.07873
Analysis of IL6R_pg_ug_2015_LN.
- processing AsymptSympt
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + Stroke_history +
Peripheral.interv, family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) Age SmokerCurrentyes Med.Statin.LLDyes Med.all.antiplateletyes
1.76707 0.02495 0.57986 -0.67730 -1.31015
Stroke_history Peripheral.interv
0.94026 -0.52936
Degrees of Freedom: 623 Total (i.e. Null); 617 Residual
Null Deviance: 466.3
Residual Deviance: 433.7 AIC: 447.7
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + hsCRP_plasma, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-3.0583 0.2511 0.4164 0.5773 1.1120
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.586e+01 1.568e+03 0.010 0.99193
currentDF[, PROTEIN] -1.244e-01 1.119e-01 -1.112 0.26595
Age 2.752e-02 1.627e-02 1.691 0.09078 .
Gendermale -1.480e-01 2.847e-01 -0.520 0.60320
Hypertension.compositeyes -1.468e-01 4.214e-01 -0.348 0.72755
DiabetesStatusDiabetes 1.144e-01 3.217e-01 0.356 0.72215
SmokerCurrentyes 5.730e-01 2.950e-01 1.942 0.05213 .
Med.Statin.LLDyes -7.764e-01 3.715e-01 -2.090 0.03662 *
Med.all.antiplateletyes -1.315e+00 7.495e-01 -1.755 0.07927 .
GFR_MDRD 8.654e-03 7.329e-03 1.181 0.23774
BMI -3.648e-02 3.444e-02 -1.059 0.28940
CAD_history 3.017e-01 2.950e-01 1.023 0.30641
Stroke_history 9.386e-01 3.373e-01 2.783 0.00539 **
Peripheral.interv -5.299e-01 2.939e-01 -1.803 0.07135 .
stenose50-70% -1.320e+01 1.568e+03 -0.008 0.99328
stenose70-90% -1.409e+01 1.568e+03 -0.009 0.99283
stenose90-99% -1.395e+01 1.568e+03 -0.009 0.99290
stenose100% (Occlusion) -3.580e-01 1.942e+03 0.000 0.99985
stenose50-99% -7.396e-02 2.294e+03 0.000 0.99997
stenose70-99% 2.370e-02 1.835e+03 0.000 0.99999
hsCRP_plasma 1.943e-03 4.408e-03 0.441 0.65942
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 466.30 on 623 degrees of freedom
Residual deviance: 423.43 on 603 degrees of freedom
AIC: 465.43
Number of Fisher Scoring iterations: 15
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_LN ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6R_pg_ug_2015_LN
Trait/outcome.............: AsymptSympt
Effect size...............: -0.12443
Standard error............: 0.111854
Odds ratio (effect size)..: 0.883
Lower 95% CI..............: 0.709
Upper 95% CI..............: 1.099
Z-value...................: -1.112435
P-value...................: 0.2659513
Hosmer and Lemeshow r^2...: 0.091937
Cox and Snell r^2.........: 0.066396
Nagelkerke's pseudo r^2...: 0.126144
Sample size of AE DB......: 2388
Sample size of model......: 624
Missing data %............: 73.86935
Analysis of MCP1_pg_ug_2015_LN.
- processing AsymptSympt
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + Stroke_history +
Peripheral.interv, family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) Age SmokerCurrentyes Med.Statin.LLDyes Med.all.antiplateletyes
1.84941 0.02409 0.58172 -0.67198 -1.32001
Stroke_history Peripheral.interv
0.93466 -0.49342
Degrees of Freedom: 642 Total (i.e. Null); 636 Residual
Null Deviance: 475.2
Residual Deviance: 442.7 AIC: 456.7
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + hsCRP_plasma, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-3.1163 0.2488 0.4146 0.5787 1.1315
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.621e+01 1.277e+03 0.013 0.9899
currentDF[, PROTEIN] 1.112e-01 9.288e-02 1.197 0.2313
Age 2.881e-02 1.614e-02 1.784 0.0744 .
Gendermale -2.003e-01 2.832e-01 -0.707 0.4794
Hypertension.compositeyes -1.115e-01 4.203e-01 -0.265 0.7908
DiabetesStatusDiabetes 1.904e-01 3.206e-01 0.594 0.5527
SmokerCurrentyes 5.819e-01 2.924e-01 1.990 0.0466 *
Med.Statin.LLDyes -7.131e-01 3.688e-01 -1.934 0.0531 .
Med.all.antiplateletyes -1.328e+00 7.486e-01 -1.774 0.0761 .
GFR_MDRD 9.876e-03 7.390e-03 1.336 0.1814
BMI -1.873e-02 3.229e-02 -0.580 0.5619
CAD_history 3.350e-01 2.910e-01 1.151 0.2496
Stroke_history 8.691e-01 3.381e-01 2.571 0.0102 *
Peripheral.interv -5.305e-01 2.903e-01 -1.827 0.0677 .
stenose50-70% -1.385e+01 1.277e+03 -0.011 0.9914
stenose70-90% -1.472e+01 1.277e+03 -0.012 0.9908
stenose90-99% -1.461e+01 1.277e+03 -0.011 0.9909
stenose100% (Occlusion) -7.127e-01 1.716e+03 0.000 0.9997
stenose50-99% -7.489e-01 2.116e+03 0.000 0.9997
stenose70-99% -5.020e-01 1.594e+03 0.000 0.9997
hsCRP_plasma 2.222e-03 4.883e-03 0.455 0.6491
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 475.20 on 642 degrees of freedom
Residual deviance: 432.23 on 622 degrees of freedom
AIC: 474.23
Number of Fisher Scoring iterations: 15
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_LN ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_LN
Trait/outcome.............: AsymptSympt
Effect size...............: 0.111174
Standard error............: 0.092877
Odds ratio (effect size)..: 1.118
Lower 95% CI..............: 0.932
Upper 95% CI..............: 1.341
Z-value...................: 1.196996
P-value...................: 0.2313082
Hosmer and Lemeshow r^2...: 0.09043
Cox and Snell r^2.........: 0.064647
Nagelkerke's pseudo r^2...: 0.123744
Sample size of AE DB......: 2388
Sample size of model......: 643
Missing data %............: 73.0737 cat("Edit the column names...\n")Edit the column names...colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
"Beta", "s.e.m.",
"OR", "low95CI", "up95CI",
"Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")
cat("Correct the variable types...\n")Correct the variable types...GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)
# Save the data
cat("Writing results to Excel-file...\n")Writing results to Excel-file...### Univariate
write.xlsx(GLM.results,
file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Multi.Protein.RANK.Symptoms.MODEL4.xlsx"),
row.names = FALSE, col.names = TRUE, sheetName = "Bin.Multi.Symptoms")
# Removing intermediates
cat("Removing intermediate files...\n")Removing intermediate files...rm(TRAIT, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)
Inverse-rank normalization
Here we use the inverse-rank normalized data - visually this is more normally distributed.
GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
PROTEIN = TRAITS.PROTEIN.RANK[protein]
cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
TRAIT = "AsymptSympt"
cat(paste0("\n- processing ",TRAIT,"\n\n"))
currentDF <- as.data.frame(AEDB.CEA %>%
dplyr::select(., PROTEIN, TRAIT, COVARIATES_M4) %>%
filter(complete.cases(.))) %>%
filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
# for debug
# print(DT::datatable(currentDF))
# print(nrow(currentDF))
# print(str(currentDF))
# print(class(currentDF[,TRAIT]))
### univariate
fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose + hsCRP_plasma,
data = currentDF, family = binomial(link = "logit"))
model_step <- stepAIC(fit, direction = "both", trace = FALSE)
print(model_step)
print(summary(fit))
GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
GLM.results = rbind(GLM.results, GLM.results.TEMP)
}
Analysis of IL6_rank.
- processing AsymptSympt
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Hypertension.composite + Med.Statin.LLD + Stroke_history +
Peripheral.interv, family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Hypertension.compositeyes Med.Statin.LLDyes Stroke_history
2.7478 -0.2817 -0.9979 -0.8465 1.3302
Peripheral.interv
-0.7109
Degrees of Freedom: 268 Total (i.e. Null); 263 Residual
Null Deviance: 275.2
Residual Deviance: 247.3 AIC: 259.3
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + hsCRP_plasma, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.3945 0.1672 0.4741 0.7406 1.2867
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.901e+01 2.400e+03 0.008 0.99368
currentDF[, PROTEIN] -3.246e-01 1.770e-01 -1.834 0.06665 .
Age -1.434e-02 2.272e-02 -0.631 0.52803
Gendermale 1.039e-01 3.677e-01 0.283 0.77755
Hypertension.compositeyes -1.357e+00 6.078e-01 -2.233 0.02555 *
DiabetesStatusDiabetes 3.891e-01 4.643e-01 0.838 0.40205
SmokerCurrentyes 1.193e-01 3.606e-01 0.331 0.74072
Med.Statin.LLDyes -9.307e-01 4.151e-01 -2.242 0.02494 *
Med.all.antiplateletyes -2.950e-01 7.226e-01 -0.408 0.68311
GFR_MDRD 7.384e-03 1.048e-02 0.704 0.48124
BMI 2.260e-02 4.391e-02 0.515 0.60684
CAD_history 2.683e-01 3.647e-01 0.736 0.46200
Stroke_history 1.403e+00 4.646e-01 3.020 0.00253 **
Peripheral.interv -7.912e-01 3.610e-01 -2.192 0.02841 *
stenose50-70% -1.700e+01 2.400e+03 -0.007 0.99435
stenose70-90% -1.646e+01 2.400e+03 -0.007 0.99453
stenose90-99% -1.579e+01 2.400e+03 -0.007 0.99475
stenose100% (Occlusion) -1.474e+00 2.645e+03 -0.001 0.99956
hsCRP_plasma 2.218e-03 4.303e-03 0.515 0.60621
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 275.20 on 268 degrees of freedom
Residual deviance: 238.94 on 250 degrees of freedom
AIC: 276.94
Number of Fisher Scoring iterations: 15
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_rank ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_rank
Trait/outcome.............: AsymptSympt
Effect size...............: -0.324614
Standard error............: 0.176996
Odds ratio (effect size)..: 0.723
Lower 95% CI..............: 0.511
Upper 95% CI..............: 1.023
Z-value...................: -1.834017
P-value...................: 0.06665144
Hosmer and Lemeshow r^2...: 0.131783
Cox and Snell r^2.........: 0.12613
Nagelkerke's pseudo r^2...: 0.196921
Sample size of AE DB......: 2388
Sample size of model......: 269
Missing data %............: 88.73534
Analysis of MCP1_rank.
- processing AsymptSympt
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Hypertension.composite +
Med.Statin.LLD + Stroke_history + Peripheral.interv, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) Hypertension.compositeyes Med.Statin.LLDyes Stroke_history Peripheral.interv
2.7459 -0.8823 -0.8903 1.3551 -0.7452
Degrees of Freedom: 282 Total (i.e. Null); 278 Residual
Null Deviance: 278.7
Residual Deviance: 251.5 AIC: 261.5
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + hsCRP_plasma, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.5354 0.2446 0.4647 0.7125 1.3983
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.852e+01 2.400e+03 0.008 0.99384
currentDF[, PROTEIN] 2.083e-01 1.698e-01 1.227 0.21985
Age -1.254e-02 2.242e-02 -0.559 0.57605
Gendermale 8.477e-02 3.694e-01 0.229 0.81849
Hypertension.compositeyes -1.129e+00 5.986e-01 -1.886 0.05935 .
DiabetesStatusDiabetes 4.580e-01 4.514e-01 1.015 0.31029
SmokerCurrentyes -6.676e-03 3.575e-01 -0.019 0.98510
Med.Statin.LLDyes -9.166e-01 4.335e-01 -2.115 0.03447 *
Med.all.antiplateletyes -5.242e-01 7.147e-01 -0.733 0.46329
GFR_MDRD -1.484e-03 1.014e-02 -0.146 0.88363
BMI 2.330e-02 4.168e-02 0.559 0.57614
CAD_history 2.614e-01 3.661e-01 0.714 0.47514
Stroke_history 1.473e+00 4.626e-01 3.185 0.00145 **
Peripheral.interv -7.791e-01 3.664e-01 -2.126 0.03349 *
stenose50-70% -1.515e+01 2.400e+03 -0.006 0.99496
stenose70-90% -1.530e+01 2.400e+03 -0.006 0.99491
stenose90-99% -1.479e+01 2.400e+03 -0.006 0.99508
stenose100% (Occlusion) -3.797e-01 2.689e+03 0.000 0.99989
hsCRP_plasma 1.105e-03 3.283e-03 0.337 0.73637
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 278.73 on 282 degrees of freedom
Residual deviance: 243.89 on 264 degrees of freedom
AIC: 281.89
Number of Fisher Scoring iterations: 15
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: AsymptSympt
Effect size...............: 0.208288
Standard error............: 0.169766
Odds ratio (effect size)..: 1.232
Lower 95% CI..............: 0.883
Upper 95% CI..............: 1.718
Z-value...................: 1.226914
P-value...................: 0.2198548
Hosmer and Lemeshow r^2...: 0.124996
Cox and Snell r^2.........: 0.115836
Nagelkerke's pseudo r^2...: 0.184884
Sample size of AE DB......: 2388
Sample size of model......: 283
Missing data %............: 88.14908
Analysis of IL6_pg_ug_2015_rank.
- processing AsymptSympt
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + CAD_history + Stroke_history +
Peripheral.interv, family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) SmokerCurrentyes Med.Statin.LLDyes Med.all.antiplateletyes CAD_history
3.5223 0.4197 -0.7448 -1.3388 0.4457
Stroke_history Peripheral.interv
1.0675 -0.6632
Degrees of Freedom: 618 Total (i.e. Null); 612 Residual
Null Deviance: 449.1
Residual Deviance: 418 AIC: 432
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + hsCRP_plasma, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-3.0372 0.2573 0.4134 0.5576 1.0672
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.665e+01 1.294e+03 0.013 0.9897
currentDF[, PROTEIN] 8.355e-02 1.349e-01 0.619 0.5358
Age 2.094e-02 1.651e-02 1.268 0.2047
Gendermale -1.585e-01 2.913e-01 -0.544 0.5864
Hypertension.compositeyes -2.428e-01 4.429e-01 -0.548 0.5836
DiabetesStatusDiabetes 1.095e-01 3.301e-01 0.332 0.7400
SmokerCurrentyes 4.602e-01 3.004e-01 1.532 0.1255
Med.Statin.LLDyes -6.981e-01 3.703e-01 -1.885 0.0594 .
Med.all.antiplateletyes -1.301e+00 7.504e-01 -1.734 0.0828 .
GFR_MDRD 7.369e-03 7.470e-03 0.987 0.3238
BMI -1.151e-02 3.352e-02 -0.343 0.7313
CAD_history 4.680e-01 3.075e-01 1.522 0.1280
Stroke_history 9.339e-01 3.521e-01 2.652 0.0080 **
Peripheral.interv -6.329e-01 2.948e-01 -2.147 0.0318 *
stenose50-70% -1.383e+01 1.294e+03 -0.011 0.9915
stenose70-90% -1.462e+01 1.294e+03 -0.011 0.9910
stenose90-99% -1.460e+01 1.294e+03 -0.011 0.9910
stenose100% (Occlusion) -8.536e-01 1.728e+03 0.000 0.9996
stenose70-99% -2.603e-01 1.601e+03 0.000 0.9999
hsCRP_plasma 2.019e-03 4.558e-03 0.443 0.6577
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 449.13 on 618 degrees of freedom
Residual deviance: 410.18 on 599 degrees of freedom
AIC: 450.18
Number of Fisher Scoring iterations: 15
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_rank ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_pg_ug_2015_rank
Trait/outcome.............: AsymptSympt
Effect size...............: 0.08355
Standard error............: 0.134925
Odds ratio (effect size)..: 1.087
Lower 95% CI..............: 0.835
Upper 95% CI..............: 1.416
Z-value...................: 0.619236
P-value...................: 0.5357609
Hosmer and Lemeshow r^2...: 0.086712
Cox and Snell r^2.........: 0.060977
Nagelkerke's pseudo r^2...: 0.118184
Sample size of AE DB......: 2388
Sample size of model......: 619
Missing data %............: 74.07873
Analysis of IL6R_pg_ug_2015_rank.
- processing AsymptSympt
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + Stroke_history +
Peripheral.interv, family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) Age SmokerCurrentyes Med.Statin.LLDyes Med.all.antiplateletyes
1.76707 0.02495 0.57986 -0.67730 -1.31015
Stroke_history Peripheral.interv
0.94026 -0.52936
Degrees of Freedom: 623 Total (i.e. Null); 617 Residual
Null Deviance: 466.3
Residual Deviance: 433.7 AIC: 447.7
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + hsCRP_plasma, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-3.0600 0.2532 0.4185 0.5752 1.1086
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.617e+01 1.557e+03 0.010 0.9917
currentDF[, PROTEIN] -1.120e-01 1.242e-01 -0.902 0.3670
Age 2.750e-02 1.628e-02 1.689 0.0912 .
Gendermale -1.439e-01 2.846e-01 -0.506 0.6132
Hypertension.compositeyes -1.462e-01 4.213e-01 -0.347 0.7285
DiabetesStatusDiabetes 1.245e-01 3.212e-01 0.388 0.6983
SmokerCurrentyes 5.751e-01 2.950e-01 1.950 0.0512 .
Med.Statin.LLDyes -7.720e-01 3.717e-01 -2.077 0.0378 *
Med.all.antiplateletyes -1.318e+00 7.493e-01 -1.758 0.0787 .
GFR_MDRD 8.610e-03 7.337e-03 1.174 0.2406
BMI -3.572e-02 3.447e-02 -1.036 0.3000
CAD_history 3.093e-01 2.947e-01 1.049 0.2940
Stroke_history 9.321e-01 3.372e-01 2.764 0.0057 **
Peripheral.interv -5.352e-01 2.940e-01 -1.820 0.0687 .
stenose50-70% -1.328e+01 1.557e+03 -0.009 0.9932
stenose70-90% -1.417e+01 1.557e+03 -0.009 0.9927
stenose90-99% -1.404e+01 1.557e+03 -0.009 0.9928
stenose100% (Occlusion) -4.252e-01 1.933e+03 0.000 0.9998
stenose50-99% -1.688e-01 2.287e+03 0.000 0.9999
stenose70-99% 1.041e-04 1.825e+03 0.000 1.0000
hsCRP_plasma 1.943e-03 4.401e-03 0.442 0.6588
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 466.30 on 623 degrees of freedom
Residual deviance: 423.92 on 603 degrees of freedom
AIC: 465.92
Number of Fisher Scoring iterations: 15
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_rank ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6R_pg_ug_2015_rank
Trait/outcome.............: AsymptSympt
Effect size...............: -0.112022
Standard error............: 0.124189
Odds ratio (effect size)..: 0.894
Lower 95% CI..............: 0.701
Upper 95% CI..............: 1.14
Z-value...................: -0.902032
P-value...................: 0.36704
Hosmer and Lemeshow r^2...: 0.090899
Cox and Snell r^2.........: 0.065671
Nagelkerke's pseudo r^2...: 0.124768
Sample size of AE DB......: 2388
Sample size of model......: 624
Missing data %............: 73.86935
Analysis of MCP1_pg_ug_2015_rank.
- processing AsymptSympt
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + Stroke_history +
Peripheral.interv, family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) Age SmokerCurrentyes Med.Statin.LLDyes Med.all.antiplateletyes
1.84941 0.02409 0.58172 -0.67198 -1.32001
Stroke_history Peripheral.interv
0.93466 -0.49342
Degrees of Freedom: 642 Total (i.e. Null); 636 Residual
Null Deviance: 475.2
Residual Deviance: 442.7 AIC: 456.7
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + hsCRP_plasma, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-3.1203 0.2480 0.4177 0.5780 1.1392
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.605e+01 1.276e+03 0.013 0.9900
currentDF[, PROTEIN] 1.655e-01 1.228e-01 1.347 0.1780
Age 2.913e-02 1.617e-02 1.801 0.0716 .
Gendermale -2.021e-01 2.832e-01 -0.714 0.4755
Hypertension.compositeyes -1.069e-01 4.204e-01 -0.254 0.7992
DiabetesStatusDiabetes 1.868e-01 3.205e-01 0.583 0.5600
SmokerCurrentyes 5.855e-01 2.925e-01 2.002 0.0453 *
Med.Statin.LLDyes -7.137e-01 3.689e-01 -1.935 0.0530 .
Med.all.antiplateletyes -1.331e+00 7.487e-01 -1.777 0.0755 .
GFR_MDRD 9.981e-03 7.402e-03 1.348 0.1775
BMI -1.865e-02 3.227e-02 -0.578 0.5634
CAD_history 3.335e-01 2.908e-01 1.147 0.2514
Stroke_history 8.659e-01 3.381e-01 2.561 0.0104 *
Peripheral.interv -5.307e-01 2.904e-01 -1.828 0.0676 .
stenose50-70% -1.385e+01 1.276e+03 -0.011 0.9913
stenose70-90% -1.473e+01 1.276e+03 -0.012 0.9908
stenose90-99% -1.461e+01 1.276e+03 -0.011 0.9909
stenose100% (Occlusion) -7.054e-01 1.716e+03 0.000 0.9997
stenose50-99% -7.583e-01 2.116e+03 0.000 0.9997
stenose70-99% -5.285e-01 1.593e+03 0.000 0.9997
hsCRP_plasma 2.253e-03 4.931e-03 0.457 0.6478
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 475.20 on 642 degrees of freedom
Residual deviance: 431.82 on 622 degrees of freedom
AIC: 473.82
Number of Fisher Scoring iterations: 15
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: AsymptSympt
Effect size...............: 0.165464
Standard error............: 0.12284
Odds ratio (effect size)..: 1.18
Lower 95% CI..............: 0.927
Upper 95% CI..............: 1.501
Z-value...................: 1.346985
P-value...................: 0.177985
Hosmer and Lemeshow r^2...: 0.091292
Cox and Snell r^2.........: 0.065243
Nagelkerke's pseudo r^2...: 0.124883
Sample size of AE DB......: 2388
Sample size of model......: 643
Missing data %............: 73.0737 cat("Edit the column names...\n")Edit the column names...colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
"Beta", "s.e.m.",
"OR", "low95CI", "up95CI",
"Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")
cat("Correct the variable types...\n")Correct the variable types...GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)
# Save the data
cat("Writing results to Excel-file...\n")Writing results to Excel-file...### Univariate
write.xlsx(GLM.results,
file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Multi.Protein.RANK.Symptoms.MODEL4.xlsx"),
row.names = FALSE, col.names = TRUE, sheetName = "Bin.Multi.Symptoms")
# Removing intermediates
cat("Removing intermediate files...\n")Removing intermediate files...rm(TRAIT, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)
Model 5
In this model we correct for Age, Gender, Hypertension status, Diabetes status, current smoker status, lipid-lowering drugs (LLDs), antiplatelet medication, eGFR (MDRD), BMI, CAD history, stroke history, peripheral interventions, stenosis., and IL6 in plaques.
Natural log-transformed data
First we use the natural-log transformed data.
GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN)) {
PROTEIN = TRAITS.PROTEIN[protein]
cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
TRAIT = "AsymptSympt"
cat(paste0("\n- processing ",TRAIT,"\n\n"))
currentDF <- as.data.frame(AEDB.CEA %>%
dplyr::select(., PROTEIN, TRAIT, COVARIATES_M5) %>%
filter(complete.cases(.))) %>%
filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
# for debug
# print(DT::datatable(currentDF))
# print(nrow(currentDF))
# print(str(currentDF))
# print(class(currentDF[,TRAIT]))
### univariate
fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose + IL6_pg_ug_2015_LN,
data = currentDF, family = binomial(link = "logit"))
model_step <- stepAIC(fit, direction = "both", trace = FALSE)
print(model_step)
print(summary(fit))
GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
GLM.results = rbind(GLM.results, GLM.results.TEMP)
}
Analysis of IL6_LN.
- processing AsymptSympt
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Hypertension.composite +
Med.Statin.LLD + Stroke_history + Peripheral.interv, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) Hypertension.compositeyes Med.Statin.LLDyes Stroke_history Peripheral.interv
3.1233 -1.1555 -0.5724 1.4408 -0.9856
Degrees of Freedom: 318 Total (i.e. Null); 314 Residual
Null Deviance: 273.6
Residual Deviance: 245.6 AIC: 255.6
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + IL6_pg_ug_2015_LN, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.6954 0.2310 0.4158 0.6161 1.3095
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 2.024e+01 1.635e+03 0.012 0.99012
currentDF[, PROTEIN] 8.990e-02 1.712e-01 0.525 0.59955
Age -1.184e-02 2.234e-02 -0.530 0.59604
Gendermale 3.202e-01 3.733e-01 0.858 0.39092
Hypertension.compositeyes -1.258e+00 7.819e-01 -1.609 0.10758
DiabetesStatusDiabetes 1.895e-01 4.358e-01 0.435 0.66367
SmokerCurrentyes -6.166e-01 3.694e-01 -1.669 0.09504 .
Med.Statin.LLDyes -5.971e-01 4.308e-01 -1.386 0.16580
Med.all.antiplateletyes -7.127e-01 6.897e-01 -1.033 0.30149
GFR_MDRD 3.517e-03 1.020e-02 0.345 0.73016
BMI -2.071e-02 4.695e-02 -0.441 0.65911
CAD_history -1.294e-01 3.774e-01 -0.343 0.73165
Stroke_history 1.525e+00 4.737e-01 3.219 0.00128 **
Peripheral.interv -9.504e-01 3.695e-01 -2.572 0.01010 *
stenose50-70% -1.466e+01 1.635e+03 -0.009 0.99284
stenose70-90% -1.547e+01 1.635e+03 -0.009 0.99245
stenose90-99% -1.514e+01 1.635e+03 -0.009 0.99261
stenose100% (Occlusion) -1.725e+00 2.046e+03 -0.001 0.99933
IL6_pg_ug_2015_LN 1.081e-01 1.190e-01 0.908 0.36387
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 273.65 on 318 degrees of freedom
Residual deviance: 236.90 on 300 degrees of freedom
AIC: 274.9
Number of Fisher Scoring iterations: 15
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_LN ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_LN
Trait/outcome.............: AsymptSympt
Effect size...............: 0.089896
Standard error............: 0.171214
Odds ratio (effect size)..: 1.094
Lower 95% CI..............: 0.782
Upper 95% CI..............: 1.53
Z-value...................: 0.525049
P-value...................: 0.5995494
Hosmer and Lemeshow r^2...: 0.134276
Cox and Snell r^2.........: 0.108799
Nagelkerke's pseudo r^2...: 0.188914
Sample size of AE DB......: 2388
Sample size of model......: 319
Missing data %............: 86.64154
Analysis of MCP1_LN.
- processing AsymptSympt
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Hypertension.composite +
Stroke_history + Peripheral.interv, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) Hypertension.compositeyes Stroke_history Peripheral.interv
3.046 -1.489 1.618 -1.024
Degrees of Freedom: 390 Total (i.e. Null); 387 Residual
Null Deviance: 317.6
Residual Deviance: 282.5 AIC: 290.5
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + IL6_pg_ug_2015_LN, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.7663 0.2017 0.3715 0.6050 1.4386
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.896e+01 1.643e+03 0.012 0.990793
currentDF[, PROTEIN] 1.215e-01 1.958e-01 0.621 0.534683
Age 2.906e-03 2.022e-02 0.144 0.885728
Gendermale -6.048e-03 3.525e-01 -0.017 0.986312
Hypertension.compositeyes -1.665e+00 7.758e-01 -2.146 0.031910 *
DiabetesStatusDiabetes 1.615e-01 3.926e-01 0.411 0.680858
SmokerCurrentyes -4.023e-01 3.387e-01 -1.188 0.234883
Med.Statin.LLDyes -4.024e-01 4.046e-01 -0.995 0.319878
Med.all.antiplateletyes -8.924e-01 6.929e-01 -1.288 0.197789
GFR_MDRD 9.595e-04 9.306e-03 0.103 0.917883
BMI 4.773e-05 4.263e-02 0.001 0.999107
CAD_history -1.512e-01 3.476e-01 -0.435 0.663556
Stroke_history 1.687e+00 4.656e-01 3.623 0.000291 ***
Peripheral.interv -1.048e+00 3.573e-01 -2.935 0.003338 **
stenose50-70% -1.416e+01 1.643e+03 -0.009 0.993126
stenose70-90% -1.554e+01 1.643e+03 -0.009 0.992453
stenose90-99% -1.488e+01 1.643e+03 -0.009 0.992774
stenose100% (Occlusion) -1.543e+00 1.975e+03 -0.001 0.999376
IL6_pg_ug_2015_LN 4.199e-02 1.094e-01 0.384 0.701204
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 317.62 on 390 degrees of freedom
Residual deviance: 271.14 on 372 degrees of freedom
AIC: 309.14
Number of Fisher Scoring iterations: 15
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_LN ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_LN
Trait/outcome.............: AsymptSympt
Effect size...............: 0.121548
Standard error............: 0.195769
Odds ratio (effect size)..: 1.129
Lower 95% CI..............: 0.769
Upper 95% CI..............: 1.657
Z-value...................: 0.620873
P-value...................: 0.534683
Hosmer and Lemeshow r^2...: 0.146359
Cox and Snell r^2.........: 0.112097
Nagelkerke's pseudo r^2...: 0.201548
Sample size of AE DB......: 2388
Sample size of model......: 391
Missing data %............: 83.62647
Analysis of IL6_pg_ug_2015_LN.
- processing AsymptSympt
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + Gender +
Med.all.antiplatelet + GFR_MDRD + Stroke_history + Peripheral.interv +
stenose, family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) Age Gendermale Med.all.antiplateletyes GFR_MDRD
15.387606 0.027762 -0.453161 -0.916880 0.008104
Stroke_history Peripheral.interv stenose50-70% stenose70-90% stenose90-99%
1.166727 -0.673292 -13.748714 -14.870639 -14.701591
stenose100% (Occlusion) stenose50-99% stenose70-99%
-0.331218 -16.941956 -0.167408
Degrees of Freedom: 1008 Total (i.e. Null); 996 Residual
Null Deviance: 707.6
Residual Deviance: 652.1 AIC: 678.1
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + IL6_pg_ug_2015_LN, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.8226 0.2652 0.4017 0.5590 0.9792
Coefficients: (1 not defined because of singularities)
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.565e+01 1.027e+03 0.015 0.987838
currentDF[, PROTEIN] 4.009e-02 7.151e-02 0.561 0.575025
Age 3.157e-02 1.296e-02 2.437 0.014805 *
Gendermale -4.000e-01 2.443e-01 -1.637 0.101631
Hypertension.compositeyes -2.988e-01 3.648e-01 -0.819 0.412745
DiabetesStatusDiabetes -8.362e-03 2.509e-01 -0.033 0.973407
SmokerCurrentyes 1.551e-01 2.332e-01 0.665 0.506105
Med.Statin.LLDyes -1.772e-01 2.732e-01 -0.649 0.516525
Med.all.antiplateletyes -9.046e-01 4.485e-01 -2.017 0.043704 *
GFR_MDRD 7.273e-03 5.677e-03 1.281 0.200167
BMI 9.375e-04 2.893e-02 0.032 0.974147
CAD_history -1.784e-01 2.263e-01 -0.788 0.430588
Stroke_history 1.108e+00 2.862e-01 3.872 0.000108 ***
Peripheral.interv -6.278e-01 2.398e-01 -2.618 0.008851 **
stenose50-70% -1.368e+01 1.027e+03 -0.013 0.989373
stenose70-90% -1.486e+01 1.027e+03 -0.014 0.988456
stenose90-99% -1.469e+01 1.027e+03 -0.014 0.988585
stenose100% (Occlusion) -4.360e-01 1.306e+03 0.000 0.999734
stenose50-99% -1.691e+01 1.027e+03 -0.016 0.986861
stenose70-99% -1.378e-01 1.233e+03 0.000 0.999911
IL6_pg_ug_2015_LN NA NA NA NA
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 707.63 on 1008 degrees of freedom
Residual deviance: 648.67 on 989 degrees of freedom
AIC: 688.67
Number of Fisher Scoring iterations: 15
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_LN ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_pg_ug_2015_LN
Trait/outcome.............: AsymptSympt
Effect size...............: 0.040092
Standard error............: 0.071508
Odds ratio (effect size)..: 1.041
Lower 95% CI..............: 0.905
Upper 95% CI..............: 1.198
Z-value...................: 0.560667
P-value...................: 0.5750247
Hosmer and Lemeshow r^2...: 0.083324
Cox and Snell r^2.........: 0.056762
Nagelkerke's pseudo r^2...: 0.112607
Sample size of AE DB......: 2388
Sample size of model......: 1009
Missing data %............: 57.74707
Analysis of IL6R_pg_ug_2015_LN.
- processing AsymptSympt
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Med.all.antiplatelet + Stroke_history + Peripheral.interv +
IL6_pg_ug_2015_LN, family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Age Gendermale Med.all.antiplateletyes
1.22809 -0.29703 0.02273 -0.38267 -0.74254
Stroke_history Peripheral.interv IL6_pg_ug_2015_LN
1.09881 -0.56737 0.13789
Degrees of Freedom: 975 Total (i.e. Null); 968 Residual
Null Deviance: 687.4
Residual Deviance: 639.4 AIC: 655.4
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + IL6_pg_ug_2015_LN, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.7313 0.2616 0.3936 0.5448 1.0748
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.547e+01 1.142e+03 0.014 0.989188
currentDF[, PROTEIN] -2.799e-01 1.087e-01 -2.575 0.010020 *
Age 3.013e-02 1.333e-02 2.259 0.023854 *
Gendermale -3.800e-01 2.498e-01 -1.521 0.128141
Hypertension.compositeyes -2.389e-01 3.670e-01 -0.651 0.515128
DiabetesStatusDiabetes -7.154e-02 2.537e-01 -0.282 0.777963
SmokerCurrentyes 1.609e-01 2.376e-01 0.677 0.498292
Med.Statin.LLDyes -2.106e-01 2.771e-01 -0.760 0.447143
Med.all.antiplateletyes -8.279e-01 4.507e-01 -1.837 0.066229 .
GFR_MDRD 5.094e-03 5.859e-03 0.869 0.384626
BMI -5.086e-03 3.024e-02 -0.168 0.866441
CAD_history -2.107e-01 2.317e-01 -0.909 0.363109
Stroke_history 1.078e+00 2.873e-01 3.752 0.000175 ***
Peripheral.interv -5.651e-01 2.460e-01 -2.297 0.021601 *
stenose50-70% -1.349e+01 1.142e+03 -0.012 0.990570
stenose70-90% -1.461e+01 1.142e+03 -0.013 0.989789
stenose90-99% -1.445e+01 1.142e+03 -0.013 0.989902
stenose100% (Occlusion) -2.736e-01 1.393e+03 0.000 0.999843
stenose50-99% -1.670e+01 1.142e+03 -0.015 0.988331
stenose70-99% -1.888e-01 1.332e+03 0.000 0.999887
IL6_pg_ug_2015_LN 1.301e-01 7.939e-02 1.639 0.101187
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 687.36 on 975 degrees of freedom
Residual deviance: 624.01 on 955 degrees of freedom
AIC: 666.01
Number of Fisher Scoring iterations: 15
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_LN ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6R_pg_ug_2015_LN
Trait/outcome.............: AsymptSympt
Effect size...............: -0.279855
Standard error............: 0.108675
Odds ratio (effect size)..: 0.756
Lower 95% CI..............: 0.611
Upper 95% CI..............: 0.935
Z-value...................: -2.575155
P-value...................: 0.0100195
Hosmer and Lemeshow r^2...: 0.092164
Cox and Snell r^2.........: 0.062846
Nagelkerke's pseudo r^2...: 0.124318
Sample size of AE DB......: 2388
Sample size of model......: 976
Missing data %............: 59.12898
Analysis of MCP1_pg_ug_2015_LN.
- processing AsymptSympt
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Med.all.antiplatelet + GFR_MDRD + Stroke_history +
Peripheral.interv + stenose, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Age Gendermale Med.all.antiplateletyes
15.571730 0.136390 0.028145 -0.459467 -0.915731
GFR_MDRD Stroke_history Peripheral.interv stenose50-70% stenose70-90%
0.008321 1.146387 -0.669062 -13.779325 -14.925585
stenose90-99% stenose100% (Occlusion) stenose50-99% stenose70-99%
-14.736397 -0.347463 -17.087502 -0.329992
Degrees of Freedom: 1007 Total (i.e. Null); 994 Residual
Null Deviance: 707.4
Residual Deviance: 648.6 AIC: 676.6
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + IL6_pg_ug_2015_LN, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.9070 0.2640 0.3966 0.5542 1.1087
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.566e+01 1.029e+03 0.015 0.987849
currentDF[, PROTEIN] 1.355e-01 8.956e-02 1.513 0.130306
Age 3.141e-02 1.298e-02 2.420 0.015530 *
Gendermale -4.097e-01 2.445e-01 -1.675 0.093857 .
Hypertension.compositeyes -2.809e-01 3.657e-01 -0.768 0.442438
DiabetesStatusDiabetes -2.837e-03 2.509e-01 -0.011 0.990980
SmokerCurrentyes 1.636e-01 2.333e-01 0.701 0.483158
Med.Statin.LLDyes -1.508e-01 2.740e-01 -0.550 0.582051
Med.all.antiplateletyes -9.121e-01 4.501e-01 -2.026 0.042717 *
GFR_MDRD 7.303e-03 5.673e-03 1.287 0.197982
BMI 2.607e-04 2.867e-02 0.009 0.992745
CAD_history -1.829e-01 2.266e-01 -0.807 0.419512
Stroke_history 1.109e+00 2.864e-01 3.873 0.000107 ***
Peripheral.interv -6.424e-01 2.407e-01 -2.669 0.007611 **
stenose50-70% -1.370e+01 1.029e+03 -0.013 0.989373
stenose70-90% -1.489e+01 1.029e+03 -0.014 0.988451
stenose90-99% -1.470e+01 1.029e+03 -0.014 0.988593
stenose100% (Occlusion) -3.751e-01 1.311e+03 0.000 0.999772
stenose50-99% -1.704e+01 1.029e+03 -0.017 0.986781
stenose70-99% -2.841e-01 1.236e+03 0.000 0.999817
IL6_pg_ug_2015_LN -1.685e-02 8.271e-02 -0.204 0.838531
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 707.39 on 1007 degrees of freedom
Residual deviance: 645.86 on 987 degrees of freedom
AIC: 687.86
Number of Fisher Scoring iterations: 15
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_LN ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_LN
Trait/outcome.............: AsymptSympt
Effect size...............: 0.1355
Standard error............: 0.089563
Odds ratio (effect size)..: 1.145
Lower 95% CI..............: 0.961
Upper 95% CI..............: 1.365
Z-value...................: 1.512895
P-value...................: 0.1303063
Hosmer and Lemeshow r^2...: 0.08698
Cox and Snell r^2.........: 0.059215
Nagelkerke's pseudo r^2...: 0.117422
Sample size of AE DB......: 2388
Sample size of model......: 1008
Missing data %............: 57.78894 cat("Edit the column names...\n")Edit the column names...colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
"Beta", "s.e.m.",
"OR", "low95CI", "up95CI",
"Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")
cat("Correct the variable types...\n")Correct the variable types...GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)
# Save the data
cat("Writing results to Excel-file...\n")Writing results to Excel-file...### Univariate
write.xlsx(GLM.results,
file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Multi.Protein.RANK.Symptoms.MODEL5.xlsx"),
row.names = FALSE, col.names = TRUE, sheetName = "Bin.Multi.Symptoms")
# Removing intermediates
cat("Removing intermediate files...\n")Removing intermediate files...rm(TRAIT, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)
Inverse-rank normalization
Here we use the inverse-rank normalized data - visually this is more normally distributed.
GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
PROTEIN = TRAITS.PROTEIN.RANK[protein]
cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
TRAIT = "AsymptSympt"
cat(paste0("\n- processing ",TRAIT,"\n\n"))
currentDF <- as.data.frame(AEDB.CEA %>%
dplyr::select(., PROTEIN, TRAIT, COVARIATES_M5rank) %>%
filter(complete.cases(.))) %>%
filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
# for debug
# print(DT::datatable(currentDF))
# print(nrow(currentDF))
# print(str(currentDF))
# print(class(currentDF[,TRAIT]))
### univariate
fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
CAD_history + Stroke_history + Peripheral.interv + stenose + IL6_pg_ug_2015_rank,
data = currentDF, family = binomial(link = "logit"))
model_step <- stepAIC(fit, direction = "both", trace = FALSE)
print(model_step)
print(summary(fit))
GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
GLM.results = rbind(GLM.results, GLM.results.TEMP)
}
Analysis of IL6_rank.
- processing AsymptSympt
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Hypertension.composite +
Stroke_history + Peripheral.interv, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) Hypertension.compositeyes Stroke_history Peripheral.interv
2.8970 -1.4467 1.5894 -0.8629
Degrees of Freedom: 370 Total (i.e. Null); 367 Residual
Null Deviance: 318.3
Residual Deviance: 287 AIC: 295
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + IL6_pg_ug_2015_rank, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.7233 0.2313 0.3958 0.6502 1.3548
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.840e+01 1.654e+03 0.011 0.991125
currentDF[, PROTEIN] 5.567e-02 1.702e-01 0.327 0.743551
Age 6.865e-03 1.952e-02 0.352 0.725052
Gendermale 2.965e-02 3.434e-01 0.086 0.931207
Hypertension.compositeyes -1.535e+00 7.735e-01 -1.985 0.047130 *
DiabetesStatusDiabetes 2.731e-02 3.801e-01 0.072 0.942721
SmokerCurrentyes -2.411e-01 3.319e-01 -0.726 0.467620
Med.Statin.LLDyes -3.834e-01 3.892e-01 -0.985 0.324634
Med.all.antiplateletyes -8.233e-01 6.737e-01 -1.222 0.221727
GFR_MDRD 3.604e-03 9.165e-03 0.393 0.694154
BMI 1.329e-03 4.227e-02 0.031 0.974915
CAD_history -2.050e-02 3.402e-01 -0.060 0.951956
Stroke_history 1.614e+00 4.619e-01 3.494 0.000476 ***
Peripheral.interv -8.466e-01 3.480e-01 -2.433 0.014983 *
stenose50-70% -1.424e+01 1.654e+03 -0.009 0.993130
stenose70-90% -1.538e+01 1.654e+03 -0.009 0.992582
stenose90-99% -1.486e+01 1.654e+03 -0.009 0.992833
stenose100% (Occlusion) -1.410e+00 1.991e+03 -0.001 0.999435
IL6_pg_ug_2015_rank 9.545e-02 1.578e-01 0.605 0.545359
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 318.3 on 370 degrees of freedom
Residual deviance: 277.7 on 352 degrees of freedom
AIC: 315.7
Number of Fisher Scoring iterations: 15
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_rank ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_rank
Trait/outcome.............: AsymptSympt
Effect size...............: 0.055668
Standard error............: 0.170159
Odds ratio (effect size)..: 1.057
Lower 95% CI..............: 0.757
Upper 95% CI..............: 1.476
Z-value...................: 0.327155
P-value...................: 0.7435508
Hosmer and Lemeshow r^2...: 0.127539
Cox and Snell r^2.........: 0.103646
Nagelkerke's pseudo r^2...: 0.179953
Sample size of AE DB......: 2388
Sample size of model......: 371
Missing data %............: 84.46399
Analysis of MCP1_rank.
- processing AsymptSympt
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Hypertension.composite +
Med.Statin.LLD + Stroke_history + Peripheral.interv, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) Hypertension.compositeyes Med.Statin.LLDyes Stroke_history Peripheral.interv
3.4064 -1.4720 -0.5627 1.6787 -0.9431
Degrees of Freedom: 394 Total (i.e. Null); 390 Residual
Null Deviance: 326
Residual Deviance: 287.9 AIC: 297.9
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + IL6_pg_ug_2015_rank, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.8502 0.2019 0.3580 0.6163 1.3684
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.855e+01 1.631e+03 0.011 0.990925
currentDF[, PROTEIN] 2.259e-01 1.705e-01 1.325 0.185209
Age 1.073e-02 1.956e-02 0.549 0.583218
Gendermale -3.045e-02 3.460e-01 -0.088 0.929859
Hypertension.compositeyes -1.639e+00 7.745e-01 -2.116 0.034335 *
DiabetesStatusDiabetes 5.711e-03 3.765e-01 0.015 0.987899
SmokerCurrentyes -2.974e-01 3.337e-01 -0.891 0.372744
Med.Statin.LLDyes -4.032e-01 4.024e-01 -1.002 0.316352
Med.all.antiplateletyes -1.040e+00 6.833e-01 -1.522 0.128006
GFR_MDRD 2.505e-03 9.230e-03 0.271 0.786073
BMI 8.869e-03 4.176e-02 0.212 0.831806
CAD_history -1.018e-01 3.459e-01 -0.294 0.768495
Stroke_history 1.677e+00 4.631e-01 3.622 0.000293 ***
Peripheral.interv -9.896e-01 3.549e-01 -2.788 0.005304 **
stenose50-70% -1.405e+01 1.631e+03 -0.009 0.993125
stenose70-90% -1.546e+01 1.631e+03 -0.009 0.992438
stenose90-99% -1.489e+01 1.631e+03 -0.009 0.992716
stenose100% (Occlusion) -1.435e+00 1.960e+03 -0.001 0.999416
IL6_pg_ug_2015_rank 5.195e-02 1.594e-01 0.326 0.744452
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 326.03 on 394 degrees of freedom
Residual deviance: 277.58 on 376 degrees of freedom
AIC: 315.58
Number of Fisher Scoring iterations: 15
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: AsymptSympt
Effect size...............: 0.225877
Standard error............: 0.170488
Odds ratio (effect size)..: 1.253
Lower 95% CI..............: 0.897
Upper 95% CI..............: 1.751
Z-value...................: 1.324886
P-value...................: 0.1852089
Hosmer and Lemeshow r^2...: 0.148606
Cox and Snell r^2.........: 0.115435
Nagelkerke's pseudo r^2...: 0.205422
Sample size of AE DB......: 2388
Sample size of model......: 395
Missing data %............: 83.45896
Analysis of IL6_pg_ug_2015_rank.
- processing AsymptSympt
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + Gender +
Med.all.antiplatelet + GFR_MDRD + Stroke_history + Peripheral.interv +
stenose, family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) Age Gendermale Med.all.antiplateletyes GFR_MDRD
15.387606 0.027762 -0.453161 -0.916880 0.008104
Stroke_history Peripheral.interv stenose50-70% stenose70-90% stenose90-99%
1.166727 -0.673292 -13.748714 -14.870639 -14.701591
stenose100% (Occlusion) stenose50-99% stenose70-99%
-0.331218 -16.941956 -0.167408
Degrees of Freedom: 1008 Total (i.e. Null); 996 Residual
Null Deviance: 707.6
Residual Deviance: 652.1 AIC: 678.1
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + IL6_pg_ug_2015_rank, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.8197 0.2654 0.4021 0.5591 0.9812
Coefficients: (1 not defined because of singularities)
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.553e+01 1.027e+03 0.015 0.987936
currentDF[, PROTEIN] 6.163e-02 1.053e-01 0.585 0.558336
Age 3.155e-02 1.295e-02 2.436 0.014857 *
Gendermale -4.004e-01 2.443e-01 -1.639 0.101287
Hypertension.compositeyes -2.983e-01 3.648e-01 -0.818 0.413500
DiabetesStatusDiabetes -8.036e-03 2.508e-01 -0.032 0.974442
SmokerCurrentyes 1.551e-01 2.332e-01 0.665 0.505947
Med.Statin.LLDyes -1.778e-01 2.733e-01 -0.651 0.515164
Med.all.antiplateletyes -9.050e-01 4.485e-01 -2.018 0.043614 *
GFR_MDRD 7.277e-03 5.677e-03 1.282 0.199907
BMI 9.863e-04 2.892e-02 0.034 0.972798
CAD_history -1.788e-01 2.262e-01 -0.790 0.429472
Stroke_history 1.108e+00 2.862e-01 3.870 0.000109 ***
Peripheral.interv -6.265e-01 2.400e-01 -2.611 0.009031 **
stenose50-70% -1.368e+01 1.027e+03 -0.013 0.989371
stenose70-90% -1.486e+01 1.027e+03 -0.014 0.988453
stenose90-99% -1.469e+01 1.027e+03 -0.014 0.988582
stenose100% (Occlusion) -4.303e-01 1.306e+03 0.000 0.999737
stenose50-99% -1.691e+01 1.027e+03 -0.016 0.986859
stenose70-99% -1.372e-01 1.233e+03 0.000 0.999911
IL6_pg_ug_2015_rank NA NA NA NA
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 707.63 on 1008 degrees of freedom
Residual deviance: 648.64 on 989 degrees of freedom
AIC: 688.64
Number of Fisher Scoring iterations: 15
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_rank ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6_pg_ug_2015_rank
Trait/outcome.............: AsymptSympt
Effect size...............: 0.061634
Standard error............: 0.1053
Odds ratio (effect size)..: 1.064
Lower 95% CI..............: 0.865
Upper 95% CI..............: 1.307
Z-value...................: 0.585315
P-value...................: 0.5583357
Hosmer and Lemeshow r^2...: 0.083364
Cox and Snell r^2.........: 0.056789
Nagelkerke's pseudo r^2...: 0.11266
Sample size of AE DB......: 2388
Sample size of model......: 1009
Missing data %............: 57.74707
Analysis of IL6R_pg_ug_2015_rank.
- processing AsymptSympt
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Med.all.antiplatelet + Stroke_history + Peripheral.interv +
IL6_pg_ug_2015_rank, family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Age Gendermale Med.all.antiplateletyes
1.3871 -0.2676 0.0228 -0.3736 -0.7581
Stroke_history Peripheral.interv IL6_pg_ug_2015_rank
1.0967 -0.5702 0.1770
Degrees of Freedom: 976 Total (i.e. Null); 969 Residual
Null Deviance: 687.6
Residual Deviance: 642 AIC: 658
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + IL6_pg_ug_2015_rank, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.7434 0.2654 0.3984 0.5457 1.0557
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.564e+01 1.146e+03 0.014 0.989110
currentDF[, PROTEIN] -2.501e-01 1.134e-01 -2.206 0.027368 *
Age 3.026e-02 1.330e-02 2.275 0.022891 *
Gendermale -3.735e-01 2.495e-01 -1.497 0.134481
Hypertension.compositeyes -2.396e-01 3.668e-01 -0.653 0.513499
DiabetesStatusDiabetes -6.182e-02 2.532e-01 -0.244 0.807128
SmokerCurrentyes 1.670e-01 2.372e-01 0.704 0.481234
Med.Statin.LLDyes -2.058e-01 2.771e-01 -0.743 0.457665
Med.all.antiplateletyes -8.375e-01 4.504e-01 -1.859 0.062968 .
GFR_MDRD 5.091e-03 5.857e-03 0.869 0.384726
BMI -4.588e-03 3.030e-02 -0.151 0.879620
CAD_history -2.014e-01 2.313e-01 -0.871 0.383855
Stroke_history 1.077e+00 2.871e-01 3.752 0.000175 ***
Peripheral.interv -5.706e-01 2.460e-01 -2.320 0.020362 *
stenose50-70% -1.354e+01 1.146e+03 -0.012 0.990573
stenose70-90% -1.466e+01 1.146e+03 -0.013 0.989793
stenose90-99% -1.451e+01 1.146e+03 -0.013 0.989900
stenose100% (Occlusion) -3.044e-01 1.398e+03 0.000 0.999826
stenose50-99% -1.678e+01 1.146e+03 -0.015 0.988317
stenose70-99% -1.591e-01 1.336e+03 0.000 0.999905
IL6_pg_ug_2015_rank 1.666e-01 1.149e-01 1.450 0.146970
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 687.60 on 976 degrees of freedom
Residual deviance: 626.44 on 956 degrees of freedom
AIC: 668.44
Number of Fisher Scoring iterations: 15
Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_rank ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: IL6R_pg_ug_2015_rank
Trait/outcome.............: AsymptSympt
Effect size...............: -0.250127
Standard error............: 0.113373
Odds ratio (effect size)..: 0.779
Lower 95% CI..............: 0.624
Upper 95% CI..............: 0.972
Z-value...................: -2.206227
P-value...................: 0.02736813
Hosmer and Lemeshow r^2...: 0.088951
Cox and Snell r^2.........: 0.060684
Nagelkerke's pseudo r^2...: 0.120096
Sample size of AE DB......: 2388
Sample size of model......: 977
Missing data %............: 59.0871
Analysis of MCP1_pg_ug_2015_rank.
- processing AsymptSympt
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Med.all.antiplatelet + GFR_MDRD + Stroke_history +
Peripheral.interv + stenose, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Age Gendermale Med.all.antiplateletyes
15.413005 0.201735 0.028292 -0.458713 -0.916140
GFR_MDRD Stroke_history Peripheral.interv stenose50-70% stenose70-90%
0.008298 1.144339 -0.666402 -13.789340 -14.939503
stenose90-99% stenose100% (Occlusion) stenose50-99% stenose70-99%
-14.747482 -0.356754 -17.110247 -0.362039
Degrees of Freedom: 1007 Total (i.e. Null); 994 Residual
Null Deviance: 707.4
Residual Deviance: 647.9 AIC: 675.9
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + IL6_pg_ug_2015_rank, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.9165 0.2626 0.3994 0.5531 1.1042
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.556e+01 1.028e+03 0.015 0.98791
currentDF[, PROTEIN] 2.053e-01 1.190e-01 1.726 0.08434 .
Age 3.153e-02 1.298e-02 2.429 0.01516 *
Gendermale -4.083e-01 2.445e-01 -1.670 0.09493 .
Hypertension.compositeyes -2.765e-01 3.659e-01 -0.756 0.44980
DiabetesStatusDiabetes -5.170e-03 2.510e-01 -0.021 0.98357
SmokerCurrentyes 1.651e-01 2.335e-01 0.707 0.47941
Med.Statin.LLDyes -1.524e-01 2.741e-01 -0.556 0.57839
Med.all.antiplateletyes -9.139e-01 4.503e-01 -2.029 0.04241 *
GFR_MDRD 7.258e-03 5.677e-03 1.278 0.20109
BMI 1.501e-04 2.864e-02 0.005 0.99582
CAD_history -1.852e-01 2.266e-01 -0.817 0.41384
Stroke_history 1.108e+00 2.864e-01 3.868 0.00011 ***
Peripheral.interv -6.415e-01 2.408e-01 -2.664 0.00773 **
stenose50-70% -1.371e+01 1.028e+03 -0.013 0.98936
stenose70-90% -1.490e+01 1.028e+03 -0.015 0.98843
stenose90-99% -1.471e+01 1.028e+03 -0.014 0.98858
stenose100% (Occlusion) -3.816e-01 1.310e+03 0.000 0.99977
stenose50-99% -1.707e+01 1.028e+03 -0.017 0.98675
stenose70-99% -3.171e-01 1.235e+03 0.000 0.99980
IL6_pg_ug_2015_rank -3.253e-02 1.209e-01 -0.269 0.78795
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 707.39 on 1007 degrees of freedom
Residual deviance: 645.10 on 987 degrees of freedom
AIC: 687.1
Number of Fisher Scoring iterations: 15
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: AsymptSympt
Effect size...............: 0.205327
Standard error............: 0.118957
Odds ratio (effect size)..: 1.228
Lower 95% CI..............: 0.973
Upper 95% CI..............: 1.55
Z-value...................: 1.726055
P-value...................: 0.08433759
Hosmer and Lemeshow r^2...: 0.088063
Cox and Snell r^2.........: 0.05993
Nagelkerke's pseudo r^2...: 0.118838
Sample size of AE DB......: 2388
Sample size of model......: 1008
Missing data %............: 57.78894 cat("Edit the column names...\n")Edit the column names...colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
"Beta", "s.e.m.",
"OR", "low95CI", "up95CI",
"Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")
cat("Correct the variable types...\n")Correct the variable types...GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)
# Save the data
cat("Writing results to Excel-file...\n")Writing results to Excel-file...### Univariate
write.xlsx(GLM.results,
file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Multi.Protein.RANK.Symptoms.MODEL5.xlsx"),
row.names = FALSE, col.names = TRUE, sheetName = "Bin.Multi.Symptoms")
# Removing intermediates
cat("Removing intermediate files...\n")Removing intermediate files...rm(TRAIT, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)
C. Longitudinal analysis secondary clinical outcome
For the longitudinal analyses of plaque and serum MCP1, IL6, and IL6R levels and secondary cardiovascular events over a three-year follow-up period.
The primary outcome is defined as “a composite of fatal or non-fatal myocardial infarction, fatal or non-fatal stroke, ruptured aortic aneurysm, fatal cardiac failure, coronary or peripheral interventions, leg amputation due to vascular causes, and cardiovascular death”, i.e. major adverse cardiovascular events (MACE). Variable: epmajor.3years, these include: - myocardial infarction (MI) - cerebral infarction (CVA/stroke) - cardiovascular death (exact cause to be investigated) - cerebral bleeding (CVA/stroke) - fatal myocardial infarction (MI) - fatal cerebral infarction - fatal cerebral bleeding - sudden death - fatal heart failure - fatal aneurysm rupture - other cardiovascular death..
The secondary outcomes will be
- incidence of fatal or non-fatal stroke (ischemic and bleeding) - variable:
epstroke.3years, these include:- cerebral infarction (CVA/stroke)
- cerebral bleeding (CVA/stroke)
- fatal cerebral infarction
- fatal cerebral bleeding.
- incidence of acute coronary events (fatal or non-fatal myocardial infarction, coronary interventions) - variable:
epcoronary.3years, these include:- myocardial infarction (MI)
- coronary angioplasty (PCI/PTCA)
- cardiovascular death (exact cause to be investigated)
- coronary bypass (CABG)
- fatal myocardial infarction (MI)
- sudden death.
- cardiovascular death - variable:
epcvdeath.3years, these include:- cardiovascular death (exact cause to be investigated)
- fatal myocardial infarction (MI)
- fatal cerebral infarction
- fatal cerebral bleeding
- sudden death
- fatal heart failure
- fatal aneurysm rupture
- other cardiovascular death..
Sanity checks
First we do some sanity checks and inventory the time-to-event and event variables.
# Reference: https://bioconductor.org/packages/devel/bioc/vignettes/MultiAssayExperiment/inst/doc/QuickStartMultiAssay.html
# If you want to suppress warnings and messages when installing/loading packages
# suppressPackageStartupMessages({})
install.packages.auto("survival")
install.packages.auto("survminer")
install.packages.auto("Hmisc")
cat("* Creating function to summarize Cox regression and prepare container for results.")* Creating function to summarize Cox regression and prepare container for results.# Function to get summary statistics from Cox regression model
COX.STAT <- function(coxfit, DATASET, OUTCOME, protein){
cat("Summarizing Cox regression results for '", protein ,"' and its association to '",OUTCOME,"' in '",DATASET,"'.\n")
if (nrow(summary(coxfit)$coefficients) == 1) {
output = c(protein, rep(NA,8))
cat("Model not fitted; probably singular.\n")
}else {
cat("Collecting data.\n\n")
cox.sum <- summary(coxfit)
cox.effectsize = cox.sum$coefficients[1,1]
cox.SE = cox.sum$coefficients[1,3]
cox.HReffect = cox.sum$coefficients[1,2]
cox.CI_low = exp(cox.effectsize - 1.96 * cox.SE)
cox.CI_up = exp(cox.effectsize + 1.96 * cox.SE)
cox.zvalue = cox.sum$coefficients[1,4]
cox.pvalue = cox.sum$coefficients[1,5]
cox.sample_size = cox.sum$n
cox.nevents = cox.sum$nevent
output = c(DATASET, OUTCOME, protein, cox.effectsize, cox.SE, cox.HReffect, cox.CI_low, cox.CI_up, cox.zvalue, cox.pvalue, cox.sample_size, cox.nevents)
cat("We have collected the following:\n")
cat("Dataset used..............:", DATASET, "\n")
cat("Outcome analyzed..........:", OUTCOME, "\n")
cat("Protein...................:", protein, "\n")
cat("Effect size...............:", round(cox.effectsize, 6), "\n")
cat("Standard error............:", round(cox.SE, 6), "\n")
cat("Odds ratio (effect size)..:", round(cox.HReffect, 3), "\n")
cat("Lower 95% CI..............:", round(cox.CI_low, 3), "\n")
cat("Upper 95% CI..............:", round(cox.CI_up, 3), "\n")
cat("T-value...................:", round(cox.zvalue, 6), "\n")
cat("P-value...................:", signif(cox.pvalue, 8), "\n")
cat("Sample size in model......:", cox.sample_size, "\n")
cat("Number of events..........:", cox.nevents, "\n")
}
return(output)
print(output)
}
times = c("ep_major_t_3years",
"ep_stroke_t_3years", "ep_coronary_t_3years", "ep_cvdeath_t_3years")
endpoints = c("epmajor.3years",
"epstroke.3years", "epcoronary.3years", "epcvdeath.3years")
cat("* Check the cases per event type - for sanity.")* Check the cases per event type - for sanity.for (events in endpoints){
print(paste0("Printing the summary of: ",events))
print(summary(AEDB.CEA[,events]))
print(table(AEDB.CEA[,events]))
}[1] "Printing the summary of: epmajor.3years"
epmajor.3years
Min. :0.0000
1st Qu.:0.0000
Median :0.0000
Mean :0.1145
3rd Qu.:0.0000
Max. :1.0000
NA's :127
0 1
2002 259
[1] "Printing the summary of: epstroke.3years"
epstroke.3years
Min. :0.00000
1st Qu.:0.00000
Median :0.00000
Mean :0.05659
3rd Qu.:0.00000
Max. :1.00000
NA's :126
0 1
2134 128
[1] "Printing the summary of: epcoronary.3years"
epcoronary.3years
Min. :0.00000
1st Qu.:0.00000
Median :0.00000
Mean :0.07825
3rd Qu.:0.00000
Max. :1.00000
NA's :126
0 1
2085 177
[1] "Printing the summary of: epcvdeath.3years"
epcvdeath.3years
Min. :0.00000
1st Qu.:0.00000
Median :0.00000
Mean :0.03892
3rd Qu.:0.00000
Max. :1.00000
NA's :127
0 1
2173 88 cat("* Check distribution of events over time - for sanity.")* Check distribution of events over time - for sanity.for (eventtimes in times){
print(paste0("Printing the summary of: ",eventtimes))
print(summary(AEDB.CEA[,eventtimes]))
}[1] "Printing the summary of: ep_major_t_3years"
ep_major_t_3years
Min. :0.000
1st Qu.:2.770
Median :3.000
Mean :2.585
3rd Qu.:3.000
Max. :3.000
NA's :129
[1] "Printing the summary of: ep_stroke_t_3years"
ep_stroke_t_3years
Min. :0.000
1st Qu.:2.890
Median :3.000
Mean :2.636
3rd Qu.:3.000
Max. :3.000
NA's :129
[1] "Printing the summary of: ep_coronary_t_3years"
ep_coronary_t_3years
Min. :0.000
1st Qu.:2.851
Median :3.000
Mean :2.637
3rd Qu.:3.000
Max. :3.000
NA's :129
[1] "Printing the summary of: ep_cvdeath_t_3years"
ep_cvdeath_t_3years
Min. :0.00274
1st Qu.:2.91781
Median :3.00000
Mean :2.72214
3rd Qu.:3.00000
Max. :3.00000
NA's :129 for (eventtime in times){
print(paste0("Printing the distribution of: ",eventtime))
p <- gghistogram(AEDB.CEA, x = eventtime, y = "..count..",
main = eventtime, bins = 15,
xlab = "year", color = uithof_color[16], fill = uithof_color[16], ggtheme = theme_minimal())
print(p)
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.EventDistributionPerYear.",eventtime,".pdf"), plot = last_plot())
}[1] "Printing the distribution of: ep_major_t_3years"
[1] "Printing the distribution of: ep_stroke_t_3years"
[1] "Printing the distribution of: ep_coronary_t_3years"
[1] "Printing the distribution of: ep_cvdeath_t_3years"
Cox regressions
Let’s perform the actual Cox-regressions. We will apply a couple of models:
- Model 1: adjusted for age and sex
- Model 2: adjusted for age, sex, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis,
- Model 3: same to model 2, with additional adjustments for circulating CRP levels
- Model 4: same to model 2 with additional adjustment for IL6 levels in the plaque
MODEL 1
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))
# Looping over each protein/endpoint/time combination
for (i in 1:length(times)){
eptime = times[i]
ep = endpoints[i]
cat(paste0("* Analyzing the effect of plaque proteins on [",ep,"].\n"))
cat(" - creating temporary SE for this work.\n")
TEMP.DF = as.data.frame(AEDB.CEA)
cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
TEMP.DF$event <- as.integer(TEMP.DF[,ep])
TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
cat(" - making strata of each of the plaque proteins and start survival analysis.\n")
for (protein in 1:length(TRAITS.PROTEIN.RANK)){
cat(paste0(" > processing [",TRAITS.PROTEIN.RANK[protein],"]; ",protein," out of ",length(TRAITS.PROTEIN.RANK)," proteins.\n"))
# splitting into two groups
TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]] <- cut2(TEMP.DF[,TRAITS.PROTEIN.RANK[protein]], g = 2)
cat(paste0(" > cross tabulation of ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
show(table(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]))
cat(paste0("\n > fitting the model for ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
fit <- survfit(as.formula(paste0("y ~ ", TRAITS.PROTEIN.RANK[protein])), data = TEMP.DF)
cat(paste0("\n > make a Kaplan-Meier-shizzle...\n"))
# make Kaplan-Meier curve and save it
show(ggsurvplot(fit, data = TEMP.DF,
palette = c("#DB003F", "#1290D9"),
# palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
linetype = c(1,2),
# linetype = c(1,2,3,4),
# conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
pval = FALSE, pval.method = FALSE, pval.size = 4,
risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
censor = FALSE,
legend = "right",
legend.title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
legend.labs = c("low", "high"),
title = paste0("Risk of ",ep,""), xlab = "Time [years]", font.main = c(16, "bold", "black")))
dev.copy2pdf(file = paste0(COX_loc,"/",
Today,".AEDB.CEA.survival.",ep,".2G.",
TRAITS.PROTEIN.RANK[protein],".pdf"), width = 12, height = 10, onefile = FALSE)
cat(paste0("\n > perform the Cox-regression fashizzle and plot it...\n"))
### Do Cox-regression and plot it
### MODEL 1 (Simple model)
cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]+Age+Gender, data = TEMP.DF)
coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]])+Age+Gender, data = TEMP.DF)
plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
# ylim = c(0.2, 1), xlim = c(0,3), col = c("#595A5C", "#DB003F", "#1290D9"),
ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
lty = c(1,2), lwd = 2,
ylab = "Suvival probability", xlab = "FU time [years]",
mark.time = FALSE, axes = FALSE, bty = "n")
legend("topright",
c("low", "high"),
title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
col = c("#DB003F", "#1290D9"),
lty = c(1,2), lwd = 2,
bty = "n")
axis(side = 1, at = seq(0, 3, by = 1))
axis(side = 2, at = seq(0, 1, by = 0.2))
dev.copy2pdf(file = paste0(COX_loc,"/",
Today,".AEDB.CEA.Cox.",ep,".2G.",
# Today,".AEDB.CEA.Cox.",ep,".4G.",
TRAITS.PROTEIN.RANK[protein],".MODEL1.pdf"), height = 12, width = 10, onefile = TRUE)
show(summary(cox))
cat(paste0("\n > writing the Cox-regression fashizzle to Excel...\n"))
COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
COX.results.TEMP[1,] = COX.STAT(cox, "AEDB.CEA", ep, TRAITS.PROTEIN.RANK[protein])
COX.results = rbind(COX.results, COX.results.TEMP)
}
}* Analyzing the effect of plaque proteins on [epmajor.3years].
- creating temporary SE for this work.
- making a 'Surv' object and adding this to temporary dataframe.
- making strata of each of the plaque proteins and start survival analysis.
> processing [IL6_rank]; 1 out of 5 proteins.
> cross tabulation of IL6_rank-stratum.
[-1.48329,0.00474) [ 0.00474,3.10694]
265 264
> fitting the model for IL6_rank-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender, data = TEMP.DF)
n= 522, number of events= 70
(1866 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00474,3.10694] 0.15269 1.16496 0.23974 0.637 0.52420
Age 0.03853 1.03928 0.01471 2.620 0.00879 **
Gendermale 0.78339 2.18888 0.32848 2.385 0.01708 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00474,3.10694] 1.165 0.8584 0.7282 1.864
Age 1.039 0.9622 1.0098 1.070
Gendermale 2.189 0.4569 1.1498 4.167
Concordance= 0.632 (se = 0.036 )
Likelihood ratio test= 14.17 on 3 df, p=0.003
Wald test = 12.77 on 3 df, p=0.005
Score (logrank) test = 13.09 on 3 df, p=0.004
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' IL6_rank ' and its association to ' epmajor.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epmajor.3years
Protein...................: IL6_rank
Effect size...............: 0.152688
Standard error............: 0.239742
Odds ratio (effect size)..: 1.165
Lower 95% CI..............: 0.728
Upper 95% CI..............: 1.864
T-value...................: 0.636883
P-value...................: 0.524201
Sample size in model......: 522
Number of events..........: 70
> processing [MCP1_rank]; 2 out of 5 proteins.
> cross tabulation of MCP1_rank-stratum.
[-2.41053,0.00444) [ 0.00444,3.12635]
283 282
> fitting the model for MCP1_rank-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender, data = TEMP.DF)
n= 558, number of events= 72
(1830 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00444,3.12635] -0.20048 0.81834 0.23746 -0.844 0.3985
Age 0.03070 1.03117 0.01446 2.123 0.0338 *
Gendermale 0.81021 2.24838 0.32813 2.469 0.0135 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00444,3.12635] 0.8183 1.2220 0.5138 1.303
Age 1.0312 0.9698 1.0024 1.061
Gendermale 2.2484 0.4448 1.1819 4.277
Concordance= 0.62 (se = 0.035 )
Likelihood ratio test= 12.85 on 3 df, p=0.005
Wald test = 11.56 on 3 df, p=0.009
Score (logrank) test = 11.89 on 3 df, p=0.008
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_rank ' and its association to ' epmajor.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epmajor.3years
Protein...................: MCP1_rank
Effect size...............: -0.200476
Standard error............: 0.237458
Odds ratio (effect size)..: 0.818
Lower 95% CI..............: 0.514
Upper 95% CI..............: 1.303
T-value...................: -0.844258
P-value...................: 0.3985254
Sample size in model......: 558
Number of events..........: 72
> processing [IL6_pg_ug_2015_rank]; 3 out of 5 proteins.
> cross tabulation of IL6_pg_ug_2015_rank-stratum.
[-3.33061,0.00109) [ 0.00109,3.33061]
577 577
> fitting the model for IL6_pg_ug_2015_rank-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender, data = TEMP.DF)
n= 1141, number of events= 133
(1247 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00109,3.33061] -0.03418 0.96640 0.17344 -0.197 0.843795
Age 0.03487 1.03549 0.01013 3.441 0.000579 ***
Gendermale 0.44653 1.56287 0.21004 2.126 0.033515 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00109,3.33061] 0.9664 1.0348 0.6879 1.358
Age 1.0355 0.9657 1.0151 1.056
Gendermale 1.5629 0.6398 1.0355 2.359
Concordance= 0.597 (se = 0.026 )
Likelihood ratio test= 17.19 on 3 df, p=6e-04
Wald test = 16.13 on 3 df, p=0.001
Score (logrank) test = 16.22 on 3 df, p=0.001
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' IL6_pg_ug_2015_rank ' and its association to ' epmajor.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epmajor.3years
Protein...................: IL6_pg_ug_2015_rank
Effect size...............: -0.034175
Standard error............: 0.173443
Odds ratio (effect size)..: 0.966
Lower 95% CI..............: 0.688
Upper 95% CI..............: 1.358
T-value...................: -0.197041
P-value...................: 0.8437952
Sample size in model......: 1141
Number of events..........: 133
> processing [IL6R_pg_ug_2015_rank]; 4 out of 5 proteins.
> cross tabulation of IL6R_pg_ug_2015_rank-stratum.
[-3.33109,0.00108) [ 0.00108,3.33109]
578 578
> fitting the model for IL6R_pg_ug_2015_rank-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender, data = TEMP.DF)
n= 1143, number of events= 137
(1245 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00108,3.33109] 0.448497 1.565956 0.174488 2.570 0.010159 *
Age 0.033432 1.033997 0.009989 3.347 0.000817 ***
Gendermale 0.335403 1.398504 0.201961 1.661 0.096766 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00108,3.33109] 1.566 0.6386 1.1124 2.204
Age 1.034 0.9671 1.0140 1.054
Gendermale 1.399 0.7150 0.9414 2.078
Concordance= 0.607 (se = 0.024 )
Likelihood ratio test= 20.48 on 3 df, p=1e-04
Wald test = 19.65 on 3 df, p=2e-04
Score (logrank) test = 19.73 on 3 df, p=2e-04
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' IL6R_pg_ug_2015_rank ' and its association to ' epmajor.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epmajor.3years
Protein...................: IL6R_pg_ug_2015_rank
Effect size...............: 0.448497
Standard error............: 0.174488
Odds ratio (effect size)..: 1.566
Lower 95% CI..............: 1.112
Upper 95% CI..............: 2.204
T-value...................: 2.570364
P-value...................: 0.01015916
Sample size in model......: 1143
Number of events..........: 137
> processing [MCP1_pg_ug_2015_rank]; 5 out of 5 proteins.
> cross tabulation of MCP1_pg_ug_2015_rank-stratum.
[-3.34148,0.00104) [ 0.00104,3.34148]
600 600
> fitting the model for MCP1_pg_ug_2015_rank-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender, data = TEMP.DF)
n= 1187, number of events= 139
(1201 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00104,3.34148] 0.037312 1.038016 0.169989 0.219 0.826266
Age 0.033060 1.033613 0.009864 3.351 0.000804 ***
Gendermale 0.343970 1.410536 0.199674 1.723 0.084950 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00104,3.34148] 1.038 0.9634 0.7439 1.448
Age 1.034 0.9675 1.0138 1.054
Gendermale 1.411 0.7090 0.9537 2.086
Concordance= 0.587 (se = 0.025 )
Likelihood ratio test= 14.94 on 3 df, p=0.002
Wald test = 14.19 on 3 df, p=0.003
Score (logrank) test = 14.26 on 3 df, p=0.003
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_pg_ug_2015_rank ' and its association to ' epmajor.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epmajor.3years
Protein...................: MCP1_pg_ug_2015_rank
Effect size...............: 0.037312
Standard error............: 0.169989
Odds ratio (effect size)..: 1.038
Lower 95% CI..............: 0.744
Upper 95% CI..............: 1.448
T-value...................: 0.219494
P-value...................: 0.8262655
Sample size in model......: 1187
Number of events..........: 139
* Analyzing the effect of plaque proteins on [epstroke.3years].
- creating temporary SE for this work.
- making a 'Surv' object and adding this to temporary dataframe.
- making strata of each of the plaque proteins and start survival analysis.
> processing [IL6_rank]; 1 out of 5 proteins.
> cross tabulation of IL6_rank-stratum.
[-1.48329,0.00474) [ 0.00474,3.10694]
265 264
> fitting the model for IL6_rank-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender, data = TEMP.DF)
n= 522, number of events= 37
(1866 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00474,3.10694] 0.07624 1.07922 0.32935 0.231 0.817
Age 0.02809 1.02849 0.01976 1.422 0.155
Gendermale 0.20807 1.23130 0.38325 0.543 0.587
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00474,3.10694] 1.079 0.9266 0.5659 2.058
Age 1.028 0.9723 0.9894 1.069
Gendermale 1.231 0.8122 0.5809 2.610
Concordance= 0.569 (se = 0.051 )
Likelihood ratio test= 2.43 on 3 df, p=0.5
Wald test = 2.35 on 3 df, p=0.5
Score (logrank) test = 2.35 on 3 df, p=0.5
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' IL6_rank ' and its association to ' epstroke.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epstroke.3years
Protein...................: IL6_rank
Effect size...............: 0.076241
Standard error............: 0.329345
Odds ratio (effect size)..: 1.079
Lower 95% CI..............: 0.566
Upper 95% CI..............: 2.058
T-value...................: 0.231493
P-value...................: 0.8169321
Sample size in model......: 522
Number of events..........: 37
> processing [MCP1_rank]; 2 out of 5 proteins.
> cross tabulation of MCP1_rank-stratum.
[-2.41053,0.00444) [ 0.00444,3.12635]
283 282
> fitting the model for MCP1_rank-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender, data = TEMP.DF)
n= 558, number of events= 38
(1830 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00444,3.12635] -0.33054 0.71854 0.32977 -1.002 0.316
Age 0.01603 1.01615 0.01930 0.830 0.406
Gendermale 0.26127 1.29858 0.38266 0.683 0.495
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00444,3.12635] 0.7185 1.3917 0.3765 1.371
Age 1.0162 0.9841 0.9784 1.055
Gendermale 1.2986 0.7701 0.6134 2.749
Concordance= 0.558 (se = 0.044 )
Likelihood ratio test= 2.22 on 3 df, p=0.5
Wald test = 2.2 on 3 df, p=0.5
Score (logrank) test = 2.21 on 3 df, p=0.5
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_rank ' and its association to ' epstroke.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epstroke.3years
Protein...................: MCP1_rank
Effect size...............: -0.330541
Standard error............: 0.329767
Odds ratio (effect size)..: 0.719
Lower 95% CI..............: 0.376
Upper 95% CI..............: 1.371
T-value...................: -1.002346
P-value...................: 0.3161763
Sample size in model......: 558
Number of events..........: 38
> processing [IL6_pg_ug_2015_rank]; 3 out of 5 proteins.
> cross tabulation of IL6_pg_ug_2015_rank-stratum.
[-3.33061,0.00109) [ 0.00109,3.33061]
577 577
> fitting the model for IL6_pg_ug_2015_rank-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender, data = TEMP.DF)
n= 1141, number of events= 69
(1247 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00109,3.33061] -0.17128 0.84258 0.24143 -0.709 0.47805
Age 0.03832 1.03907 0.01407 2.724 0.00645 **
Gendermale 0.12792 1.13646 0.26956 0.475 0.63511
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00109,3.33061] 0.8426 1.1868 0.5249 1.352
Age 1.0391 0.9624 1.0108 1.068
Gendermale 1.1365 0.8799 0.6700 1.928
Concordance= 0.601 (se = 0.035 )
Likelihood ratio test= 8.42 on 3 df, p=0.04
Wald test = 8.05 on 3 df, p=0.05
Score (logrank) test = 8.1 on 3 df, p=0.04
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' IL6_pg_ug_2015_rank ' and its association to ' epstroke.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epstroke.3years
Protein...................: IL6_pg_ug_2015_rank
Effect size...............: -0.171284
Standard error............: 0.241434
Odds ratio (effect size)..: 0.843
Lower 95% CI..............: 0.525
Upper 95% CI..............: 1.352
T-value...................: -0.709446
P-value...................: 0.4780475
Sample size in model......: 1141
Number of events..........: 69
> processing [IL6R_pg_ug_2015_rank]; 4 out of 5 proteins.
> cross tabulation of IL6R_pg_ug_2015_rank-stratum.
[-3.33109,0.00108) [ 0.00108,3.33109]
578 578
> fitting the model for IL6R_pg_ug_2015_rank-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender, data = TEMP.DF)
n= 1143, number of events= 72
(1245 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00108,3.33109] 0.37431 1.45399 0.23932 1.564 0.1178
Age 0.03294 1.03349 0.01373 2.400 0.0164 *
Gendermale 0.09556 1.10027 0.26316 0.363 0.7165
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00108,3.33109] 1.454 0.6878 0.9096 2.324
Age 1.033 0.9676 1.0061 1.062
Gendermale 1.100 0.9089 0.6569 1.843
Concordance= 0.593 (se = 0.034 )
Likelihood ratio test= 8.25 on 3 df, p=0.04
Wald test = 7.97 on 3 df, p=0.05
Score (logrank) test = 8 on 3 df, p=0.05
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' IL6R_pg_ug_2015_rank ' and its association to ' epstroke.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epstroke.3years
Protein...................: IL6R_pg_ug_2015_rank
Effect size...............: 0.374314
Standard error............: 0.239317
Odds ratio (effect size)..: 1.454
Lower 95% CI..............: 0.91
Upper 95% CI..............: 2.324
T-value...................: 1.564093
P-value...................: 0.1177957
Sample size in model......: 1143
Number of events..........: 72
> processing [MCP1_pg_ug_2015_rank]; 5 out of 5 proteins.
> cross tabulation of MCP1_pg_ug_2015_rank-stratum.
[-3.34148,0.00104) [ 0.00104,3.34148]
600 600
> fitting the model for MCP1_pg_ug_2015_rank-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender, data = TEMP.DF)
n= 1187, number of events= 73
(1201 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00104,3.34148] 0.13635 1.14608 0.23507 0.580 0.5619
Age 0.03393 1.03451 0.01354 2.505 0.0122 *
Gendermale 0.07048 1.07302 0.25901 0.272 0.7855
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00104,3.34148] 1.146 0.8725 0.7230 1.817
Age 1.035 0.9666 1.0074 1.062
Gendermale 1.073 0.9319 0.6459 1.783
Concordance= 0.595 (se = 0.033 )
Likelihood ratio test= 6.98 on 3 df, p=0.07
Wald test = 6.75 on 3 df, p=0.08
Score (logrank) test = 6.78 on 3 df, p=0.08
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_pg_ug_2015_rank ' and its association to ' epstroke.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epstroke.3years
Protein...................: MCP1_pg_ug_2015_rank
Effect size...............: 0.13635
Standard error............: 0.235072
Odds ratio (effect size)..: 1.146
Lower 95% CI..............: 0.723
Upper 95% CI..............: 1.817
T-value...................: 0.580033
P-value...................: 0.5618924
Sample size in model......: 1187
Number of events..........: 73
* Analyzing the effect of plaque proteins on [epcoronary.3years].
- creating temporary SE for this work.
- making a 'Surv' object and adding this to temporary dataframe.
- making strata of each of the plaque proteins and start survival analysis.
> processing [IL6_rank]; 1 out of 5 proteins.
> cross tabulation of IL6_rank-stratum.
[-1.48329,0.00474) [ 0.00474,3.10694]
265 264
> fitting the model for IL6_rank-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender, data = TEMP.DF)
n= 522, number of events= 47
(1866 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00474,3.10694] 0.17371 1.18970 0.29265 0.594 0.5528
Age 0.03896 1.03973 0.01792 2.175 0.0297 *
Gendermale 1.01973 2.77244 0.43719 2.332 0.0197 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00474,3.10694] 1.190 0.8405 0.6704 2.111
Age 1.040 0.9618 1.0039 1.077
Gendermale 2.772 0.3607 1.1769 6.531
Concordance= 0.646 (se = 0.038 )
Likelihood ratio test= 12.15 on 3 df, p=0.007
Wald test = 10.44 on 3 df, p=0.02
Score (logrank) test = 10.96 on 3 df, p=0.01
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' IL6_rank ' and its association to ' epcoronary.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epcoronary.3years
Protein...................: IL6_rank
Effect size...............: 0.173705
Standard error............: 0.292653
Odds ratio (effect size)..: 1.19
Lower 95% CI..............: 0.67
Upper 95% CI..............: 2.111
T-value...................: 0.593554
P-value...................: 0.5528105
Sample size in model......: 522
Number of events..........: 47
> processing [MCP1_rank]; 2 out of 5 proteins.
> cross tabulation of MCP1_rank-stratum.
[-2.41053,0.00444) [ 0.00444,3.12635]
283 282
> fitting the model for MCP1_rank-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender, data = TEMP.DF)
n= 558, number of events= 47
(1830 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00444,3.12635] 0.30417 1.35550 0.29625 1.027 0.3045
Age 0.03606 1.03672 0.01811 1.991 0.0464 *
Gendermale 0.79302 2.21007 0.41021 1.933 0.0532 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00444,3.12635] 1.356 0.7377 0.7585 2.423
Age 1.037 0.9646 1.0006 1.074
Gendermale 2.210 0.4525 0.9891 4.938
Concordance= 0.628 (se = 0.039 )
Likelihood ratio test= 9.85 on 3 df, p=0.02
Wald test = 8.75 on 3 df, p=0.03
Score (logrank) test = 8.99 on 3 df, p=0.03
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_rank ' and its association to ' epcoronary.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epcoronary.3years
Protein...................: MCP1_rank
Effect size...............: 0.304172
Standard error............: 0.296253
Odds ratio (effect size)..: 1.356
Lower 95% CI..............: 0.758
Upper 95% CI..............: 2.423
T-value...................: 1.026729
P-value...................: 0.3045479
Sample size in model......: 558
Number of events..........: 47
> processing [IL6_pg_ug_2015_rank]; 3 out of 5 proteins.
> cross tabulation of IL6_pg_ug_2015_rank-stratum.
[-3.33061,0.00109) [ 0.00109,3.33061]
577 577
> fitting the model for IL6_pg_ug_2015_rank-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender, data = TEMP.DF)
n= 1141, number of events= 89
(1247 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00109,3.33061] -0.079260 0.923800 0.212158 -0.374 0.70871
Age 0.005236 1.005250 0.011952 0.438 0.66131
Gendermale 0.776579 2.174022 0.283231 2.742 0.00611 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00109,3.33061] 0.9238 1.0825 0.6095 1.400
Age 1.0053 0.9948 0.9820 1.029
Gendermale 2.1740 0.4600 1.2479 3.787
Concordance= 0.575 (se = 0.028 )
Likelihood ratio test= 9.22 on 3 df, p=0.03
Wald test = 7.88 on 3 df, p=0.05
Score (logrank) test = 8.27 on 3 df, p=0.04
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' IL6_pg_ug_2015_rank ' and its association to ' epcoronary.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epcoronary.3years
Protein...................: IL6_pg_ug_2015_rank
Effect size...............: -0.07926
Standard error............: 0.212158
Odds ratio (effect size)..: 0.924
Lower 95% CI..............: 0.61
Upper 95% CI..............: 1.4
T-value...................: -0.373589
P-value...................: 0.7087101
Sample size in model......: 1141
Number of events..........: 89
> processing [IL6R_pg_ug_2015_rank]; 4 out of 5 proteins.
> cross tabulation of IL6R_pg_ug_2015_rank-stratum.
[-3.33109,0.00108) [ 0.00108,3.33109]
578 578
> fitting the model for IL6R_pg_ug_2015_rank-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender, data = TEMP.DF)
n= 1143, number of events= 90
(1245 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00108,3.33109] 0.382893 1.466522 0.214416 1.786 0.0741 .
Age 0.007227 1.007253 0.011916 0.607 0.5442
Gendermale 0.619255 1.857544 0.269355 2.299 0.0215 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00108,3.33109] 1.467 0.6819 0.9633 2.233
Age 1.007 0.9928 0.9840 1.031
Gendermale 1.858 0.5383 1.0956 3.149
Concordance= 0.59 (se = 0.029 )
Likelihood ratio test= 9.52 on 3 df, p=0.02
Wald test = 8.77 on 3 df, p=0.03
Score (logrank) test = 8.98 on 3 df, p=0.03
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' IL6R_pg_ug_2015_rank ' and its association to ' epcoronary.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epcoronary.3years
Protein...................: IL6R_pg_ug_2015_rank
Effect size...............: 0.382893
Standard error............: 0.214416
Odds ratio (effect size)..: 1.467
Lower 95% CI..............: 0.963
Upper 95% CI..............: 2.233
T-value...................: 1.785754
P-value...................: 0.0741391
Sample size in model......: 1143
Number of events..........: 90
> processing [MCP1_pg_ug_2015_rank]; 5 out of 5 proteins.
> cross tabulation of MCP1_pg_ug_2015_rank-stratum.
[-3.34148,0.00104) [ 0.00104,3.34148]
600 600
> fitting the model for MCP1_pg_ug_2015_rank-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender, data = TEMP.DF)
n= 1187, number of events= 91
(1201 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00104,3.34148] -0.172568 0.841501 0.210487 -0.820 0.4123
Age 0.006873 1.006897 0.011840 0.581 0.5616
Gendermale 0.671236 1.956655 0.269190 2.494 0.0126 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00104,3.34148] 0.8415 1.1884 0.5570 1.271
Age 1.0069 0.9932 0.9838 1.031
Gendermale 1.9567 0.5111 1.1545 3.316
Concordance= 0.577 (se = 0.03 )
Likelihood ratio test= 7.99 on 3 df, p=0.05
Wald test = 7.11 on 3 df, p=0.07
Score (logrank) test = 7.34 on 3 df, p=0.06
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_pg_ug_2015_rank ' and its association to ' epcoronary.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epcoronary.3years
Protein...................: MCP1_pg_ug_2015_rank
Effect size...............: -0.172568
Standard error............: 0.210487
Odds ratio (effect size)..: 0.842
Lower 95% CI..............: 0.557
Upper 95% CI..............: 1.271
T-value...................: -0.819853
P-value...................: 0.4122998
Sample size in model......: 1187
Number of events..........: 91
* Analyzing the effect of plaque proteins on [epcvdeath.3years].
- creating temporary SE for this work.
- making a 'Surv' object and adding this to temporary dataframe.
- making strata of each of the plaque proteins and start survival analysis.
> processing [IL6_rank]; 1 out of 5 proteins.
> cross tabulation of IL6_rank-stratum.
[-1.48329,0.00474) [ 0.00474,3.10694]
265 264
> fitting the model for IL6_rank-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender, data = TEMP.DF)
n= 522, number of events= 27
(1866 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00474,3.10694] 0.12032 1.12786 0.38592 0.312 0.7552
Age 0.05430 1.05581 0.02434 2.231 0.0257 *
Gendermale 0.82329 2.27798 0.54178 1.520 0.1286
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00474,3.10694] 1.128 0.8866 0.5294 2.403
Age 1.056 0.9471 1.0066 1.107
Gendermale 2.278 0.4390 0.7877 6.587
Concordance= 0.665 (se = 0.059 )
Likelihood ratio test= 8.18 on 3 df, p=0.04
Wald test = 7.35 on 3 df, p=0.06
Score (logrank) test = 7.53 on 3 df, p=0.06
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' IL6_rank ' and its association to ' epcvdeath.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epcvdeath.3years
Protein...................: IL6_rank
Effect size...............: 0.120318
Standard error............: 0.385919
Odds ratio (effect size)..: 1.128
Lower 95% CI..............: 0.529
Upper 95% CI..............: 2.403
T-value...................: 0.311771
P-value...................: 0.7552146
Sample size in model......: 522
Number of events..........: 27
> processing [MCP1_rank]; 2 out of 5 proteins.
> cross tabulation of MCP1_rank-stratum.
[-2.41053,0.00444) [ 0.00444,3.12635]
283 282
> fitting the model for MCP1_rank-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender, data = TEMP.DF)
n= 558, number of events= 27
(1830 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00444,3.12635] -0.05233 0.94902 0.38729 -0.135 0.8925
Age 0.05687 1.05852 0.02468 2.304 0.0212 *
Gendermale 0.79435 2.21300 0.54234 1.465 0.1430
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00444,3.12635] 0.949 1.0537 0.4442 2.027
Age 1.059 0.9447 1.0085 1.111
Gendermale 2.213 0.4519 0.7644 6.407
Concordance= 0.667 (se = 0.059 )
Likelihood ratio test= 8.48 on 3 df, p=0.04
Wald test = 7.69 on 3 df, p=0.05
Score (logrank) test = 7.85 on 3 df, p=0.05
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_rank ' and its association to ' epcvdeath.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epcvdeath.3years
Protein...................: MCP1_rank
Effect size...............: -0.052326
Standard error............: 0.387295
Odds ratio (effect size)..: 0.949
Lower 95% CI..............: 0.444
Upper 95% CI..............: 2.027
T-value...................: -0.135107
P-value...................: 0.8925272
Sample size in model......: 558
Number of events..........: 27
> processing [IL6_pg_ug_2015_rank]; 3 out of 5 proteins.
> cross tabulation of IL6_pg_ug_2015_rank-stratum.
[-3.33061,0.00109) [ 0.00109,3.33061]
577 577
> fitting the model for IL6_pg_ug_2015_rank-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender, data = TEMP.DF)
n= 1141, number of events= 45
(1247 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00109,3.33061] 0.10096 1.10623 0.29884 0.338 0.7355
Age 0.08294 1.08648 0.01919 4.322 1.55e-05 ***
Gendermale 0.89258 2.44141 0.41182 2.167 0.0302 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00109,3.33061] 1.106 0.9040 0.6158 1.987
Age 1.086 0.9204 1.0464 1.128
Gendermale 2.441 0.4096 1.0892 5.472
Concordance= 0.706 (se = 0.037 )
Likelihood ratio test= 26.21 on 3 df, p=9e-06
Wald test = 22.65 on 3 df, p=5e-05
Score (logrank) test = 23.27 on 3 df, p=4e-05
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' IL6_pg_ug_2015_rank ' and its association to ' epcvdeath.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epcvdeath.3years
Protein...................: IL6_pg_ug_2015_rank
Effect size...............: 0.100959
Standard error............: 0.298838
Odds ratio (effect size)..: 1.106
Lower 95% CI..............: 0.616
Upper 95% CI..............: 1.987
T-value...................: 0.337838
P-value...................: 0.7354853
Sample size in model......: 1141
Number of events..........: 45
> processing [IL6R_pg_ug_2015_rank]; 4 out of 5 proteins.
> cross tabulation of IL6R_pg_ug_2015_rank-stratum.
[-3.33109,0.00108) [ 0.00108,3.33109]
578 578
> fitting the model for IL6R_pg_ug_2015_rank-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender, data = TEMP.DF)
n= 1143, number of events= 45
(1245 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00108,3.33109] 0.77149 2.16299 0.31659 2.437 0.0148 *
Age 0.08479 1.08849 0.01926 4.403 1.07e-05 ***
Gendermale 0.87112 2.38958 0.41164 2.116 0.0343 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00108,3.33109] 2.163 0.4623 1.163 4.023
Age 1.088 0.9187 1.048 1.130
Gendermale 2.390 0.4185 1.066 5.354
Concordance= 0.727 (se = 0.037 )
Likelihood ratio test= 31.83 on 3 df, p=6e-07
Wald test = 28.21 on 3 df, p=3e-06
Score (logrank) test = 28.76 on 3 df, p=3e-06
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' IL6R_pg_ug_2015_rank ' and its association to ' epcvdeath.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epcvdeath.3years
Protein...................: IL6R_pg_ug_2015_rank
Effect size...............: 0.77149
Standard error............: 0.316594
Odds ratio (effect size)..: 2.163
Lower 95% CI..............: 1.163
Upper 95% CI..............: 4.023
T-value...................: 2.436846
P-value...................: 0.01481598
Sample size in model......: 1143
Number of events..........: 45
> processing [MCP1_pg_ug_2015_rank]; 5 out of 5 proteins.
> cross tabulation of MCP1_pg_ug_2015_rank-stratum.
[-3.34148,0.00104) [ 0.00104,3.34148]
600 600
> fitting the model for MCP1_pg_ug_2015_rank-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender, data = TEMP.DF)
n= 1187, number of events= 45
(1201 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00104,3.34148] -0.11294 0.89320 0.29879 -0.378 0.7054
Age 0.08412 1.08776 0.01929 4.360 1.3e-05 ***
Gendermale 0.90814 2.47970 0.41206 2.204 0.0275 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00104,3.34148] 0.8932 1.1196 0.4973 1.604
Age 1.0878 0.9193 1.0474 1.130
Gendermale 2.4797 0.4033 1.1057 5.561
Concordance= 0.709 (se = 0.036 )
Likelihood ratio test= 26.75 on 3 df, p=7e-06
Wald test = 23.06 on 3 df, p=4e-05
Score (logrank) test = 23.7 on 3 df, p=3e-05
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_pg_ug_2015_rank ' and its association to ' epcvdeath.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epcvdeath.3years
Protein...................: MCP1_pg_ug_2015_rank
Effect size...............: -0.112944
Standard error............: 0.298793
Odds ratio (effect size)..: 0.893
Lower 95% CI..............: 0.497
Upper 95% CI..............: 1.604
T-value...................: -0.378
P-value...................: 0.7054304
Sample size in model......: 1187
Number of events..........: 45 
cat("- Edit the column names...\n")- Edit the column names...colnames(COX.results) = c("Dataset", "Outcome", "CpG",
"Beta", "s.e.m.",
"HR", "low95CI", "up95CI",
"Z-value", "P-value", "SampleSize", "N_events")
cat("- Correct the variable types...\n")- Correct the variable types...COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)
AEDB.CEA.COX.results <- COX.results
# Save the data
cat("- Writing results to Excel-file...\n")- Writing results to Excel-file...head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AEDB.CEA.COX.results,
file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Cox.2G.MODEL1.xlsx"),
creator = "Sander W. van der Laan",
sheetName = "Results", headerStyle = head.style,
row.names = FALSE, col.names = TRUE, overwrite = TRUE)
# Removing intermediates
cat("- Removing intermediate files...\n")- Removing intermediate files...#rm(TEMP.DF, protein, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AEDB.CEA.COX.results)
#rm(head.style)
MODEL 2
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))
# Looping over each protein/endpoint/time combination
for (i in 1:length(times)){
eptime = times[i]
ep = endpoints[i]
cat(paste0("* Analyzing the effect of plaque proteins on [",ep,"].\n"))
cat(" - creating temporary SE for this work.\n")
TEMP.DF = as.data.frame(AEDB.CEA)
cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
TEMP.DF$event <- as.integer(TEMP.DF[,ep])
#as.integer(TEMP.DF[,ep] == "Excluded")
TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
cat(" - making strata of each of the plaque proteins and start survival analysis.\n")
for (protein in 1:length(TRAITS.PROTEIN.RANK)){
cat(paste0(" > processing [",TRAITS.PROTEIN.RANK[protein],"]; ",protein," out of ",length(TRAITS.PROTEIN.RANK)," proteins.\n"))
# splitting into two groups
TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]] <- cut2(TEMP.DF[,TRAITS.PROTEIN.RANK[protein]], g = 2)
cat(paste0(" > cross tabulation of ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
show(table(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]))
cat(paste0("\n > fitting the model for ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
fit <- survfit(as.formula(paste0("y ~ ", TRAITS.PROTEIN.RANK[protein])), data = TEMP.DF)
cat(paste0("\n > make a Kaplan-Meier-shizzle...\n"))
# make Kaplan-Meier curve and save it
show(ggsurvplot(fit, data = TEMP.DF,
palette = c("#DB003F", "#1290D9"),
# palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
linetype = c(1,2),
# linetype = c(1,2,3,4),
# conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
pval = FALSE, pval.method = FALSE, pval.size = 4,
risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
censor = FALSE,
legend = "right",
legend.title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
legend.labs = c("low", "high"),
title = paste0("Risk of ",ep,""), xlab = "Time [years]", font.main = c(16, "bold", "black")))
dev.copy2pdf(file = paste0(COX_loc,"/",
Today,".AEDB.CEA.survival.",ep,".2G.",
TRAITS.PROTEIN.RANK[protein],".pdf"), width = 12, height = 10, onefile = FALSE)
cat(paste0("\n > perform the Cox-regression fashizzle and plot it...\n"))
### Do Cox-regression and plot it
### MODEL 2 adjusted for age, sex, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis
cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]+Age + Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + CAD_history + Stroke_history + Peripheral.interv + stenose, data = TEMP.DF)
coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]])+Age + Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + CAD_history + Stroke_history + Peripheral.interv + stenose, data = TEMP.DF)
plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
# ylim = c(0.2, 1), xlim = c(0,3), col = c("#595A5C", "#DB003F", "#1290D9"),
ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
lty = c(1,2), lwd = 2,
ylab = "Suvival probability", xlab = "FU time [years]",
mark.time = FALSE, axes = FALSE, bty = "n")
legend("topright",
c("low", "high"),
title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
col = c("#DB003F", "#1290D9"),
lty = c(1,2), lwd = 2,
bty = "n")
axis(side = 1, at = seq(0, 3, by = 1))
axis(side = 2, at = seq(0, 1, by = 0.2))
dev.copy2pdf(file = paste0(COX_loc,"/",
Today,".AEDB.CEA.Cox.",ep,".2G.",
# Today,".AEDB.CEA.Cox.",ep,".4G.",
TRAITS.PROTEIN.RANK[protein],".MODEL2.pdf"), height = 12, width = 10, onefile = TRUE)
show(summary(cox))
cat(paste0("\n > writing the Cox-regression fashizzle to Excel...\n"))
COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
COX.results.TEMP[1,] = COX.STAT(cox, "AEDB.CEA", ep, TRAITS.PROTEIN.RANK[protein])
COX.results = rbind(COX.results, COX.results.TEMP)
}
}* Analyzing the effect of plaque proteins on [epmajor.3years].
- creating temporary SE for this work.
- making a 'Surv' object and adding this to temporary dataframe.
- making strata of each of the plaque proteins and start survival analysis.
> processing [IL6_rank]; 1 out of 5 proteins.
> cross tabulation of IL6_rank-stratum.
[-1.48329,0.00474) [ 0.00474,3.10694]
265 264
> fitting the model for IL6_rank-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose, data = TEMP.DF)
n= 477, number of events= 64
(1911 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00474,3.10694] 1.309e-01 1.140e+00 2.580e-01 0.508 0.61176
Age 4.862e-02 1.050e+00 1.825e-02 2.664 0.00773 **
Gendermale 6.880e-01 1.990e+00 3.396e-01 2.026 0.04279 *
Hypertension.compositeno -7.622e-01 4.666e-01 5.345e-01 -1.426 0.15387
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes 7.270e-01 2.069e+00 2.896e-01 2.510 0.01207 *
SmokerCurrentno -6.648e-01 5.144e-01 2.676e-01 -2.484 0.01298 *
SmokerCurrentyes NA NA 0.000e+00 NA NA
Med.Statin.LLDno 2.278e-01 1.256e+00 2.880e-01 0.791 0.42898
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 1.425e-01 1.153e+00 4.189e-01 0.340 0.73377
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD -4.913e-03 9.951e-01 6.711e-03 -0.732 0.46411
BMI 5.332e-04 1.001e+00 3.520e-02 0.015 0.98792
CAD_history 5.932e-01 1.810e+00 2.727e-01 2.176 0.02959 *
Stroke_history 1.930e-01 1.213e+00 2.673e-01 0.722 0.47032
Peripheral.interv 6.059e-02 1.062e+00 3.164e-01 0.191 0.84814
stenose0-49% -1.611e+01 1.011e-07 3.372e+03 -0.005 0.99619
stenose50-70% -1.333e+00 2.637e-01 1.461e+00 -0.912 0.36164
stenose70-90% -4.726e-01 6.234e-01 1.061e+00 -0.445 0.65609
stenose90-99% -7.412e-01 4.765e-01 1.069e+00 -0.694 0.48795
stenose100% (Occlusion) NA NA 0.000e+00 NA NA
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% NA NA 0.000e+00 NA NA
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00474,3.10694] 1.140e+00 8.773e-01 0.68752 1.8899
Age 1.050e+00 9.525e-01 1.01293 1.0881
Gendermale 1.990e+00 5.026e-01 1.02259 3.8717
Hypertension.compositeno 4.666e-01 2.143e+00 0.16368 1.3303
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 2.069e+00 4.834e-01 1.17271 3.6496
SmokerCurrentno 5.144e-01 1.944e+00 0.30442 0.8691
SmokerCurrentyes NA NA NA NA
Med.Statin.LLDno 1.256e+00 7.963e-01 0.71413 2.2084
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 1.153e+00 8.672e-01 0.50733 2.6210
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 9.951e-01 1.005e+00 0.98210 1.0083
BMI 1.001e+00 9.995e-01 0.93383 1.0720
CAD_history 1.810e+00 5.525e-01 1.06055 3.0885
Stroke_history 1.213e+00 8.245e-01 0.71822 2.0483
Peripheral.interv 1.062e+00 9.412e-01 0.57145 1.9754
stenose0-49% 1.011e-07 9.891e+06 0.00000 Inf
stenose50-70% 2.637e-01 3.792e+00 0.01505 4.6215
stenose70-90% 6.234e-01 1.604e+00 0.07787 4.9903
stenose90-99% 4.765e-01 2.098e+00 0.05868 3.8703
stenose100% (Occlusion) NA NA NA NA
stenoseNA NA NA NA NA
stenose50-99% NA NA NA NA
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
Concordance= 0.714 (se = 0.029 )
Likelihood ratio test= 37 on 17 df, p=0.003
Wald test = 33.02 on 17 df, p=0.01
Score (logrank) test = 35.81 on 17 df, p=0.005
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' IL6_rank ' and its association to ' epmajor.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epmajor.3years
Protein...................: IL6_rank
Effect size...............: 0.130932
Standard error............: 0.257961
Odds ratio (effect size)..: 1.14
Lower 95% CI..............: 0.688
Upper 95% CI..............: 1.89
T-value...................: 0.507564
P-value...................: 0.6117595
Sample size in model......: 477
Number of events..........: 64
> processing [MCP1_rank]; 2 out of 5 proteins.
> cross tabulation of MCP1_rank-stratum.
[-2.41053,0.00444) [ 0.00444,3.12635]
283 282
> fitting the model for MCP1_rank-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose, data = TEMP.DF)
n= 509, number of events= 66
(1879 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00444,3.12635] -3.049e-01 7.372e-01 2.532e-01 -1.204 0.2287
Age 3.571e-02 1.036e+00 1.769e-02 2.019 0.0435 *
Gendermale 7.112e-01 2.036e+00 3.367e-01 2.112 0.0347 *
Hypertension.compositeno -8.086e-01 4.455e-01 5.335e-01 -1.516 0.1296
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes 6.394e-01 1.895e+00 2.842e-01 2.250 0.0244 *
SmokerCurrentno -5.756e-01 5.624e-01 2.650e-01 -2.172 0.0299 *
SmokerCurrentyes NA NA 0.000e+00 NA NA
Med.Statin.LLDno 2.742e-01 1.315e+00 2.847e-01 0.963 0.3356
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 3.447e-03 1.003e+00 4.203e-01 0.008 0.9935
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD -9.214e-03 9.908e-01 6.629e-03 -1.390 0.1646
BMI 1.203e-02 1.012e+00 3.398e-02 0.354 0.7234
CAD_history 4.032e-01 1.497e+00 2.681e-01 1.504 0.1326
Stroke_history 2.966e-01 1.345e+00 2.596e-01 1.142 0.2533
Peripheral.interv 1.347e-01 1.144e+00 3.143e-01 0.429 0.6682
stenose0-49% -1.653e+01 6.613e-08 3.378e+03 -0.005 0.9961
stenose50-70% -1.817e+00 1.625e-01 1.455e+00 -1.249 0.2118
stenose70-90% -8.410e-01 4.313e-01 1.052e+00 -0.799 0.4240
stenose90-99% -1.061e+00 3.461e-01 1.061e+00 -1.000 0.3172
stenose100% (Occlusion) NA NA 0.000e+00 NA NA
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% NA NA 0.000e+00 NA NA
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00444,3.12635] 7.372e-01 1.356e+00 0.448785 1.2111
Age 1.036e+00 9.649e-01 1.001040 1.0729
Gendermale 2.036e+00 4.911e-01 1.052586 3.9395
Hypertension.compositeno 4.455e-01 2.245e+00 0.156588 1.2674
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 1.895e+00 5.276e-01 1.085923 3.3081
SmokerCurrentno 5.624e-01 1.778e+00 0.334542 0.9454
SmokerCurrentyes NA NA NA NA
Med.Statin.LLDno 1.315e+00 7.602e-01 0.752853 2.2986
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 1.003e+00 9.966e-01 0.440301 2.2869
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 9.908e-01 1.009e+00 0.978038 1.0038
BMI 1.012e+00 9.880e-01 0.946891 1.0818
CAD_history 1.497e+00 6.682e-01 0.884884 2.5311
Stroke_history 1.345e+00 7.434e-01 0.808757 2.2376
Peripheral.interv 1.144e+00 8.740e-01 0.617943 2.1187
stenose0-49% 6.613e-08 1.512e+07 0.000000 Inf
stenose50-70% 1.625e-01 6.155e+00 0.009373 2.8157
stenose70-90% 4.313e-01 2.319e+00 0.054876 3.3897
stenose90-99% 3.461e-01 2.889e+00 0.043283 2.7680
stenose100% (Occlusion) NA NA NA NA
stenoseNA NA NA NA NA
stenose50-99% NA NA NA NA
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
Concordance= 0.7 (se = 0.028 )
Likelihood ratio test= 35.01 on 17 df, p=0.006
Wald test = 31.02 on 17 df, p=0.02
Score (logrank) test = 33.48 on 17 df, p=0.01
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_rank ' and its association to ' epmajor.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epmajor.3years
Protein...................: MCP1_rank
Effect size...............: -0.304862
Standard error............: 0.253245
Odds ratio (effect size)..: 0.737
Lower 95% CI..............: 0.449
Upper 95% CI..............: 1.211
T-value...................: -1.203825
P-value...................: 0.2286573
Sample size in model......: 509
Number of events..........: 66
> processing [IL6_pg_ug_2015_rank]; 3 out of 5 proteins.
> cross tabulation of IL6_pg_ug_2015_rank-stratum.
[-3.33061,0.00109) [ 0.00109,3.33061]
577 577
> fitting the model for IL6_pg_ug_2015_rank-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose, data = TEMP.DF)
n= 1002, number of events= 114
(1386 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00109,3.33061] 9.097e-02 1.095e+00 1.899e-01 0.479 0.631870
Age 3.856e-02 1.039e+00 1.296e-02 2.975 0.002927 **
Gendermale 5.730e-01 1.774e+00 2.310e-01 2.481 0.013107 *
Hypertension.compositeno -5.174e-01 5.961e-01 3.773e-01 -1.371 0.170285
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes -4.403e-02 9.569e-01 2.233e-01 -0.197 0.843644
SmokerCurrentno -6.023e-01 5.475e-01 2.046e-01 -2.944 0.003240 **
SmokerCurrentyes NA NA 0.000e+00 NA NA
Med.Statin.LLDno 3.369e-01 1.401e+00 2.175e-01 1.549 0.121332
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 4.026e-01 1.496e+00 2.586e-01 1.557 0.119528
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD -1.899e-02 9.812e-01 4.977e-03 -3.815 0.000136 ***
BMI 5.747e-02 1.059e+00 2.597e-02 2.213 0.026905 *
CAD_history 1.411e-01 1.152e+00 2.032e-01 0.695 0.487257
Stroke_history 4.304e-02 1.044e+00 2.028e-01 0.212 0.831900
Peripheral.interv 6.318e-01 1.881e+00 2.183e-01 2.894 0.003802 **
stenose0-49% -1.561e+01 1.659e-07 2.745e+03 -0.006 0.995462
stenose50-70% -8.519e-01 4.266e-01 8.692e-01 -0.980 0.326998
stenose70-90% -2.953e-01 7.443e-01 7.264e-01 -0.406 0.684395
stenose90-99% -2.722e-01 7.617e-01 7.236e-01 -0.376 0.706782
stenose100% (Occlusion) 4.662e-02 1.048e+00 1.239e+00 0.038 0.969996
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% -1.539e+01 2.069e-07 4.208e+03 -0.004 0.997082
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00109,3.33061] 1.095e+00 9.130e-01 0.75490 1.5890
Age 1.039e+00 9.622e-01 1.01325 1.0660
Gendermale 1.774e+00 5.638e-01 1.12785 2.7890
Hypertension.compositeno 5.961e-01 1.678e+00 0.28453 1.2487
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 9.569e-01 1.045e+00 0.61778 1.4822
SmokerCurrentno 5.475e-01 1.826e+00 0.36664 0.8176
SmokerCurrentyes NA NA NA NA
Med.Statin.LLDno 1.401e+00 7.140e-01 0.91454 2.1451
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 1.496e+00 6.686e-01 0.90098 2.4831
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 9.812e-01 1.019e+00 0.97167 0.9908
BMI 1.059e+00 9.442e-01 1.00659 1.1145
CAD_history 1.152e+00 8.684e-01 0.77330 1.7150
Stroke_history 1.044e+00 9.579e-01 0.70161 1.5534
Peripheral.interv 1.881e+00 5.316e-01 1.22621 2.8853
stenose0-49% 1.659e-07 6.027e+06 0.00000 Inf
stenose50-70% 4.266e-01 2.344e+00 0.07765 2.3434
stenose70-90% 7.443e-01 1.343e+00 0.17924 3.0909
stenose90-99% 7.617e-01 1.313e+00 0.18446 3.1454
stenose100% (Occlusion) 1.048e+00 9.545e-01 0.09231 11.8918
stenoseNA NA NA NA NA
stenose50-99% 2.069e-07 4.833e+06 0.00000 Inf
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
Concordance= 0.705 (se = 0.023 )
Likelihood ratio test= 68.36 on 19 df, p=2e-07
Wald test = 38.72 on 19 df, p=0.005
Score (logrank) test = 66.86 on 19 df, p=3e-07
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' IL6_pg_ug_2015_rank ' and its association to ' epmajor.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epmajor.3years
Protein...................: IL6_pg_ug_2015_rank
Effect size...............: 0.090966
Standard error............: 0.189871
Odds ratio (effect size)..: 1.095
Lower 95% CI..............: 0.755
Upper 95% CI..............: 1.589
T-value...................: 0.479096
P-value...................: 0.6318701
Sample size in model......: 1002
Number of events..........: 114
> processing [IL6R_pg_ug_2015_rank]; 4 out of 5 proteins.
> cross tabulation of IL6R_pg_ug_2015_rank-stratum.
[-3.33109,0.00108) [ 0.00108,3.33109]
578 578
> fitting the model for IL6R_pg_ug_2015_rank-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose, data = TEMP.DF)
n= 1006, number of events= 119
(1382 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00108,3.33109] 3.149e-01 1.370e+00 1.929e-01 1.633 0.102540
Age 3.419e-02 1.035e+00 1.261e-02 2.711 0.006713 **
Gendermale 4.342e-01 1.544e+00 2.199e-01 1.974 0.048335 *
Hypertension.compositeno -4.432e-01 6.420e-01 3.576e-01 -1.239 0.215200
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes -5.992e-02 9.418e-01 2.208e-01 -0.271 0.786088
SmokerCurrentno -5.022e-01 6.052e-01 2.014e-01 -2.493 0.012653 *
SmokerCurrentyes NA NA 0.000e+00 NA NA
Med.Statin.LLDno 2.893e-01 1.336e+00 2.159e-01 1.340 0.180144
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 3.779e-01 1.459e+00 2.578e-01 1.466 0.142612
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD -1.788e-02 9.823e-01 4.918e-03 -3.635 0.000278 ***
BMI 5.699e-02 1.059e+00 2.656e-02 2.146 0.031874 *
CAD_history 1.903e-01 1.210e+00 1.989e-01 0.956 0.338836
Stroke_history 3.055e-02 1.031e+00 1.974e-01 0.155 0.876999
Peripheral.interv 5.619e-01 1.754e+00 2.131e-01 2.637 0.008368 **
stenose0-49% -1.540e+01 2.045e-07 3.123e+03 -0.005 0.996065
stenose50-70% -8.794e-01 4.150e-01 8.686e-01 -1.012 0.311330
stenose70-90% -3.666e-01 6.931e-01 7.269e-01 -0.504 0.613981
stenose90-99% -3.659e-01 6.936e-01 7.256e-01 -0.504 0.614096
stenose100% (Occlusion) -5.671e-02 9.449e-01 1.238e+00 -0.046 0.963452
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% -1.519e+01 2.528e-07 2.805e+03 -0.005 0.995679
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00108,3.33109] 1.370e+00 7.299e-01 0.93882 1.9996
Age 1.035e+00 9.664e-01 1.00951 1.0607
Gendermale 1.544e+00 6.478e-01 1.00318 2.3753
Hypertension.compositeno 6.420e-01 1.558e+00 0.31849 1.2939
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 9.418e-01 1.062e+00 0.61101 1.4518
SmokerCurrentno 6.052e-01 1.652e+00 0.40784 0.8981
SmokerCurrentyes NA NA NA NA
Med.Statin.LLDno 1.336e+00 7.488e-01 0.87479 2.0390
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 1.459e+00 6.853e-01 0.88047 2.4185
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 9.823e-01 1.018e+00 0.97286 0.9918
BMI 1.059e+00 9.446e-01 1.00495 1.1152
CAD_history 1.210e+00 8.267e-01 0.81904 1.7863
Stroke_history 1.031e+00 9.699e-01 0.70028 1.5180
Peripheral.interv 1.754e+00 5.701e-01 1.15517 2.6634
stenose0-49% 2.045e-07 4.889e+06 0.00000 Inf
stenose50-70% 4.150e-01 2.409e+00 0.07564 2.2773
stenose70-90% 6.931e-01 1.443e+00 0.16675 2.8806
stenose90-99% 6.936e-01 1.442e+00 0.16730 2.8756
stenose100% (Occlusion) 9.449e-01 1.058e+00 0.08354 10.6868
stenoseNA NA NA NA NA
stenose50-99% 2.528e-07 3.956e+06 0.00000 Inf
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
Concordance= 0.694 (se = 0.023 )
Likelihood ratio test= 64.3 on 19 df, p=8e-07
Wald test = 60.58 on 19 df, p=3e-06
Score (logrank) test = 64.14 on 19 df, p=8e-07
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' IL6R_pg_ug_2015_rank ' and its association to ' epmajor.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epmajor.3years
Protein...................: IL6R_pg_ug_2015_rank
Effect size...............: 0.314907
Standard error............: 0.19288
Odds ratio (effect size)..: 1.37
Lower 95% CI..............: 0.939
Upper 95% CI..............: 2
T-value...................: 1.632661
P-value...................: 0.1025404
Sample size in model......: 1006
Number of events..........: 119
> processing [MCP1_pg_ug_2015_rank]; 5 out of 5 proteins.
> cross tabulation of MCP1_pg_ug_2015_rank-stratum.
[-3.34148,0.00104) [ 0.00104,3.34148]
600 600
> fitting the model for MCP1_pg_ug_2015_rank-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose, data = TEMP.DF)
n= 1044, number of events= 120
(1344 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00104,3.34148] 1.748e-01 1.191e+00 1.860e-01 0.940 0.34727
Age 3.379e-02 1.034e+00 1.249e-02 2.704 0.00684 **
Gendermale 4.474e-01 1.564e+00 2.179e-01 2.053 0.04006 *
Hypertension.compositeno -4.648e-01 6.283e-01 3.578e-01 -1.299 0.19387
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes -4.790e-02 9.532e-01 2.188e-01 -0.219 0.82676
SmokerCurrentno -5.109e-01 6.000e-01 2.003e-01 -2.550 0.01076 *
SmokerCurrentyes NA NA 0.000e+00 NA NA
Med.Statin.LLDno 3.401e-01 1.405e+00 2.130e-01 1.597 0.11036
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 3.623e-01 1.437e+00 2.571e-01 1.409 0.15872
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD -1.979e-02 9.804e-01 4.879e-03 -4.056 4.99e-05 ***
BMI 5.687e-02 1.059e+00 2.555e-02 2.226 0.02603 *
CAD_history 1.679e-01 1.183e+00 1.979e-01 0.848 0.39636
Stroke_history 3.892e-02 1.040e+00 1.978e-01 0.197 0.84402
Peripheral.interv 5.626e-01 1.755e+00 2.131e-01 2.641 0.00827 **
stenose0-49% -1.548e+01 1.894e-07 2.739e+03 -0.006 0.99549
stenose50-70% -8.329e-01 4.348e-01 8.698e-01 -0.958 0.33825
stenose70-90% -2.432e-01 7.841e-01 7.270e-01 -0.334 0.73803
stenose90-99% -1.974e-01 8.209e-01 7.252e-01 -0.272 0.78546
stenose100% (Occlusion) 1.205e-01 1.128e+00 1.242e+00 0.097 0.92272
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% -1.518e+01 2.560e-07 2.870e+03 -0.005 0.99578
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00104,3.34148] 1.191e+00 8.396e-01 0.82721 1.7148
Age 1.034e+00 9.668e-01 1.00934 1.0600
Gendermale 1.564e+00 6.393e-01 1.02051 2.3975
Hypertension.compositeno 6.283e-01 1.592e+00 0.31162 1.2667
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 9.532e-01 1.049e+00 0.62075 1.4638
SmokerCurrentno 6.000e-01 1.667e+00 0.40514 0.8884
SmokerCurrentyes NA NA NA NA
Med.Statin.LLDno 1.405e+00 7.117e-01 0.92552 2.1331
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 1.437e+00 6.961e-01 0.86802 2.3778
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 9.804e-01 1.020e+00 0.97107 0.9898
BMI 1.059e+00 9.447e-01 1.00681 1.1129
CAD_history 1.183e+00 8.455e-01 0.80247 1.7433
Stroke_history 1.040e+00 9.618e-01 0.70553 1.5321
Peripheral.interv 1.755e+00 5.697e-01 1.15608 2.6651
stenose0-49% 1.894e-07 5.281e+06 0.00000 Inf
stenose50-70% 4.348e-01 2.300e+00 0.07905 2.3912
stenose70-90% 7.841e-01 1.275e+00 0.18860 3.2602
stenose90-99% 8.209e-01 1.218e+00 0.19814 3.4007
stenose100% (Occlusion) 1.128e+00 8.865e-01 0.09893 12.8612
stenoseNA NA NA NA NA
stenose50-99% 2.560e-07 3.906e+06 0.00000 Inf
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
Concordance= 0.696 (se = 0.023 )
Likelihood ratio test= 65.33 on 19 df, p=5e-07
Wald test = 60.85 on 19 df, p=3e-06
Score (logrank) test = 63.96 on 19 df, p=9e-07
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_pg_ug_2015_rank ' and its association to ' epmajor.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epmajor.3years
Protein...................: MCP1_pg_ug_2015_rank
Effect size...............: 0.174792
Standard error............: 0.185968
Odds ratio (effect size)..: 1.191
Lower 95% CI..............: 0.827
Upper 95% CI..............: 1.715
T-value...................: 0.939904
P-value...................: 0.3472667
Sample size in model......: 1044
Number of events..........: 120
* Analyzing the effect of plaque proteins on [epstroke.3years].
- creating temporary SE for this work.
- making a 'Surv' object and adding this to temporary dataframe.
- making strata of each of the plaque proteins and start survival analysis.
> processing [IL6_rank]; 1 out of 5 proteins.
> cross tabulation of IL6_rank-stratum.
[-1.48329,0.00474) [ 0.00474,3.10694]
265 264
> fitting the model for IL6_rank-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose, data = TEMP.DF)
n= 477, number of events= 32
(1911 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00474,3.10694] 2.472e-02 1.025e+00 3.648e-01 0.068 0.946
Age 3.546e-02 1.036e+00 2.467e-02 1.438 0.151
Gendermale 3.450e-02 1.035e+00 4.022e-01 0.086 0.932
Hypertension.compositeno -7.437e-01 4.754e-01 7.516e-01 -0.990 0.322
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes 5.315e-01 1.701e+00 4.207e-01 1.263 0.206
SmokerCurrentno -5.350e-01 5.857e-01 3.815e-01 -1.402 0.161
SmokerCurrentyes NA NA 0.000e+00 NA NA
Med.Statin.LLDno -2.487e-02 9.754e-01 4.179e-01 -0.060 0.953
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 3.103e-01 1.364e+00 5.848e-01 0.531 0.596
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD -8.165e-04 9.992e-01 9.773e-03 -0.084 0.933
BMI -1.288e-02 9.872e-01 4.814e-02 -0.268 0.789
CAD_history 2.559e-01 1.292e+00 3.977e-01 0.643 0.520
Stroke_history 3.207e-01 1.378e+00 3.689e-01 0.869 0.385
Peripheral.interv -5.758e-01 5.622e-01 5.473e-01 -1.052 0.293
stenose0-49% -1.810e+01 1.377e-08 1.222e+04 -0.001 0.999
stenose50-70% -1.785e+01 1.766e-08 4.805e+03 -0.004 0.997
stenose70-90% -8.478e-01 4.283e-01 1.142e+00 -0.742 0.458
stenose90-99% -9.721e-01 3.783e-01 1.153e+00 -0.843 0.399
stenose100% (Occlusion) NA NA 0.000e+00 NA NA
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% NA NA 0.000e+00 NA NA
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00474,3.10694] 1.025e+00 9.756e-01 0.50142 2.095
Age 1.036e+00 9.652e-01 0.98720 1.087
Gendermale 1.035e+00 9.661e-01 0.47054 2.277
Hypertension.compositeno 4.754e-01 2.104e+00 0.10897 2.074
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 1.701e+00 5.877e-01 0.74601 3.881
SmokerCurrentno 5.857e-01 1.708e+00 0.27727 1.237
SmokerCurrentyes NA NA NA NA
Med.Statin.LLDno 9.754e-01 1.025e+00 0.43002 2.213
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 1.364e+00 7.332e-01 0.43347 4.291
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 9.992e-01 1.001e+00 0.98023 1.019
BMI 9.872e-01 1.013e+00 0.89831 1.085
CAD_history 1.292e+00 7.742e-01 0.59235 2.816
Stroke_history 1.378e+00 7.256e-01 0.66875 2.840
Peripheral.interv 5.622e-01 1.779e+00 0.19233 1.644
stenose0-49% 1.377e-08 7.260e+07 0.00000 Inf
stenose50-70% 1.766e-08 5.662e+07 0.00000 Inf
stenose70-90% 4.283e-01 2.335e+00 0.04564 4.020
stenose90-99% 3.783e-01 2.643e+00 0.03946 3.627
stenose100% (Occlusion) NA NA NA NA
stenoseNA NA NA NA NA
stenose50-99% NA NA NA NA
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
Concordance= 0.712 (se = 0.04 )
Likelihood ratio test= 13.18 on 17 df, p=0.7
Wald test = 9.66 on 17 df, p=0.9
Score (logrank) test = 11.9 on 17 df, p=0.8
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' IL6_rank ' and its association to ' epstroke.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epstroke.3years
Protein...................: IL6_rank
Effect size...............: 0.024717
Standard error............: 0.364815
Odds ratio (effect size)..: 1.025
Lower 95% CI..............: 0.501
Upper 95% CI..............: 2.095
T-value...................: 0.067752
P-value...................: 0.9459834
Sample size in model......: 477
Number of events..........: 32
> processing [MCP1_rank]; 2 out of 5 proteins.
> cross tabulation of MCP1_rank-stratum.
[-2.41053,0.00444) [ 0.00444,3.12635]
283 282
> fitting the model for MCP1_rank-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose, data = TEMP.DF)
n= 509, number of events= 33
(1879 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00444,3.12635] -4.721e-01 6.237e-01 3.625e-01 -1.302 0.193
Age 2.320e-02 1.023e+00 2.358e-02 0.984 0.325
Gendermale 6.449e-02 1.067e+00 3.990e-01 0.162 0.872
Hypertension.compositeno -8.685e-01 4.196e-01 7.492e-01 -1.159 0.246
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes 3.761e-01 1.457e+00 4.161e-01 0.904 0.366
SmokerCurrentno -4.557e-01 6.340e-01 3.760e-01 -1.212 0.226
SmokerCurrentyes NA NA 0.000e+00 NA NA
Med.Statin.LLDno 4.243e-02 1.043e+00 4.107e-01 0.103 0.918
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 1.006e-01 1.106e+00 5.873e-01 0.171 0.864
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD 1.256e-03 1.001e+00 9.624e-03 0.130 0.896
BMI 1.816e-02 1.018e+00 4.546e-02 0.399 0.690
CAD_history 7.676e-02 1.080e+00 3.981e-01 0.193 0.847
Stroke_history 4.080e-01 1.504e+00 3.637e-01 1.122 0.262
Peripheral.interv -4.789e-01 6.195e-01 5.459e-01 -0.877 0.380
stenose0-49% -1.860e+01 8.342e-09 1.262e+04 -0.001 0.999
stenose50-70% -1.841e+01 1.006e-08 4.635e+03 -0.004 0.997
stenose70-90% -1.288e+00 2.757e-01 1.118e+00 -1.153 0.249
stenose90-99% -1.414e+00 2.433e-01 1.134e+00 -1.246 0.213
stenose100% (Occlusion) NA NA 0.000e+00 NA NA
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% NA NA 0.000e+00 NA NA
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00444,3.12635] 6.237e-01 1.603e+00 0.30651 1.269
Age 1.023e+00 9.771e-01 0.97724 1.072
Gendermale 1.067e+00 9.375e-01 0.48793 2.332
Hypertension.compositeno 4.196e-01 2.383e+00 0.09662 1.822
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 1.457e+00 6.865e-01 0.64443 3.292
SmokerCurrentno 6.340e-01 1.577e+00 0.30344 1.325
SmokerCurrentyes NA NA NA NA
Med.Statin.LLDno 1.043e+00 9.585e-01 0.46651 2.333
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 1.106e+00 9.043e-01 0.34980 3.496
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 1.001e+00 9.987e-01 0.98255 1.020
BMI 1.018e+00 9.820e-01 0.93151 1.113
CAD_history 1.080e+00 9.261e-01 0.49485 2.356
Stroke_history 1.504e+00 6.650e-01 0.73718 3.068
Peripheral.interv 6.195e-01 1.614e+00 0.21250 1.806
stenose0-49% 8.342e-09 1.199e+08 0.00000 Inf
stenose50-70% 1.006e-08 9.940e+07 0.00000 Inf
stenose70-90% 2.757e-01 3.627e+00 0.03082 2.466
stenose90-99% 2.433e-01 4.110e+00 0.02633 2.248
stenose100% (Occlusion) NA NA NA NA
stenoseNA NA NA NA NA
stenose50-99% NA NA NA NA
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
Concordance= 0.687 (se = 0.042 )
Likelihood ratio test= 13.23 on 17 df, p=0.7
Wald test = 10.29 on 17 df, p=0.9
Score (logrank) test = 12.25 on 17 df, p=0.8
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_rank ' and its association to ' epstroke.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epstroke.3years
Protein...................: MCP1_rank
Effect size...............: -0.472091
Standard error............: 0.36246
Odds ratio (effect size)..: 0.624
Lower 95% CI..............: 0.307
Upper 95% CI..............: 1.269
T-value...................: -1.302464
P-value...................: 0.1927578
Sample size in model......: 509
Number of events..........: 33
> processing [IL6_pg_ug_2015_rank]; 3 out of 5 proteins.
> cross tabulation of IL6_pg_ug_2015_rank-stratum.
[-3.33061,0.00109) [ 0.00109,3.33061]
577 577
> fitting the model for IL6_pg_ug_2015_rank-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose, data = TEMP.DF)
n= 1002, number of events= 58
(1386 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00109,3.33061] -1.438e-01 8.660e-01 2.656e-01 -0.541 0.58818
Age 5.026e-02 1.052e+00 1.793e-02 2.803 0.00506 **
Gendermale 2.919e-01 1.339e+00 3.000e-01 0.973 0.33043
Hypertension.compositeno -2.080e-01 8.122e-01 4.442e-01 -0.468 0.63956
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes -6.073e-02 9.411e-01 3.197e-01 -0.190 0.84935
SmokerCurrentno -3.427e-01 7.098e-01 2.939e-01 -1.166 0.24358
SmokerCurrentyes NA NA 0.000e+00 NA NA
Med.Statin.LLDno 3.978e-01 1.488e+00 2.912e-01 1.366 0.17192
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 3.468e-01 1.414e+00 3.748e-01 0.925 0.35485
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD -5.639e-03 9.944e-01 7.004e-03 -0.805 0.42078
BMI 9.236e-02 1.097e+00 3.357e-02 2.751 0.00593 **
CAD_history -5.750e-01 5.627e-01 3.288e-01 -1.749 0.08031 .
Stroke_history 3.241e-01 1.383e+00 2.748e-01 1.179 0.23826
Peripheral.interv 4.887e-01 1.630e+00 3.276e-01 1.492 0.13578
stenose0-49% -1.559e+01 1.699e-07 3.740e+03 -0.004 0.99667
stenose50-70% -5.898e-01 5.544e-01 1.159e+00 -0.509 0.61097
stenose70-90% -3.521e-01 7.032e-01 1.025e+00 -0.343 0.73127
stenose90-99% -3.578e-01 6.992e-01 1.024e+00 -0.349 0.72672
stenose100% (Occlusion) 3.875e-01 1.473e+00 1.439e+00 0.269 0.78767
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% -1.537e+01 2.109e-07 5.615e+03 -0.003 0.99782
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00109,3.33061] 8.660e-01 1.155e+00 0.51454 1.458
Age 1.052e+00 9.510e-01 1.01523 1.089
Gendermale 1.339e+00 7.468e-01 0.74380 2.411
Hypertension.compositeno 8.122e-01 1.231e+00 0.34009 1.940
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 9.411e-01 1.063e+00 0.50289 1.761
SmokerCurrentno 7.098e-01 1.409e+00 0.39898 1.263
SmokerCurrentyes NA NA NA NA
Med.Statin.LLDno 1.488e+00 6.718e-01 0.84120 2.634
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 1.414e+00 7.070e-01 0.67854 2.949
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 9.944e-01 1.006e+00 0.98082 1.008
BMI 1.097e+00 9.118e-01 1.02692 1.171
CAD_history 5.627e-01 1.777e+00 0.29542 1.072
Stroke_history 1.383e+00 7.232e-01 0.80691 2.370
Peripheral.interv 1.630e+00 6.134e-01 0.85779 3.098
stenose0-49% 1.699e-07 5.887e+06 0.00000 Inf
stenose50-70% 5.544e-01 1.804e+00 0.05713 5.380
stenose70-90% 7.032e-01 1.422e+00 0.09429 5.245
stenose90-99% 6.992e-01 1.430e+00 0.09401 5.200
stenose100% (Occlusion) 1.473e+00 6.788e-01 0.08784 24.711
stenoseNA NA NA NA NA
stenose50-99% 2.109e-07 4.742e+06 0.00000 Inf
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
Concordance= 0.682 (se = 0.036 )
Likelihood ratio test= 27.19 on 19 df, p=0.1
Wald test = 22.76 on 19 df, p=0.2
Score (logrank) test = 26.28 on 19 df, p=0.1
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' IL6_pg_ug_2015_rank ' and its association to ' epstroke.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epstroke.3years
Protein...................: IL6_pg_ug_2015_rank
Effect size...............: -0.143835
Standard error............: 0.265636
Odds ratio (effect size)..: 0.866
Lower 95% CI..............: 0.515
Upper 95% CI..............: 1.458
T-value...................: -0.541475
P-value...................: 0.5881802
Sample size in model......: 1002
Number of events..........: 58
> processing [IL6R_pg_ug_2015_rank]; 4 out of 5 proteins.
> cross tabulation of IL6R_pg_ug_2015_rank-stratum.
[-3.33109,0.00108) [ 0.00108,3.33109]
578 578
> fitting the model for IL6R_pg_ug_2015_rank-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose, data = TEMP.DF)
n= 1006, number of events= 61
(1382 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00108,3.33109] 9.097e-02 1.095e+00 2.649e-01 0.343 0.73127
Age 4.173e-02 1.043e+00 1.733e-02 2.408 0.01603 *
Gendermale 2.095e-01 1.233e+00 2.916e-01 0.719 0.47243
Hypertension.compositeno -1.100e-01 8.958e-01 4.146e-01 -0.265 0.79079
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes -9.138e-02 9.127e-01 3.159e-01 -0.289 0.77242
SmokerCurrentno -1.889e-01 8.279e-01 2.901e-01 -0.651 0.51499
SmokerCurrentyes NA NA 0.000e+00 NA NA
Med.Statin.LLDno 4.222e-01 1.525e+00 2.868e-01 1.472 0.14104
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 3.050e-01 1.357e+00 3.718e-01 0.820 0.41197
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD -3.051e-03 9.970e-01 7.024e-03 -0.434 0.66402
BMI 9.994e-02 1.105e+00 3.516e-02 2.842 0.00448 **
CAD_history -4.477e-01 6.391e-01 3.134e-01 -1.429 0.15306
Stroke_history 3.724e-01 1.451e+00 2.649e-01 1.406 0.15973
Peripheral.interv 5.374e-01 1.712e+00 3.163e-01 1.699 0.08937 .
stenose0-49% -1.514e+01 2.649e-07 4.536e+03 -0.003 0.99734
stenose50-70% -6.162e-01 5.400e-01 1.159e+00 -0.532 0.59478
stenose70-90% -3.140e-01 7.305e-01 1.023e+00 -0.307 0.75884
stenose90-99% -4.118e-01 6.625e-01 1.025e+00 -0.402 0.68783
stenose100% (Occlusion) 3.763e-01 1.457e+00 1.435e+00 0.262 0.79312
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% -1.530e+01 2.262e-07 3.904e+03 -0.004 0.99687
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00108,3.33109] 1.095e+00 9.130e-01 0.65167 1.841
Age 1.043e+00 9.591e-01 1.00780 1.079
Gendermale 1.233e+00 8.110e-01 0.69627 2.184
Hypertension.compositeno 8.958e-01 1.116e+00 0.39748 2.019
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 9.127e-01 1.096e+00 0.49134 1.695
SmokerCurrentno 8.279e-01 1.208e+00 0.46886 1.462
SmokerCurrentyes NA NA NA NA
Med.Statin.LLDno 1.525e+00 6.556e-01 0.86938 2.676
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 1.357e+00 7.371e-01 0.65464 2.812
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 9.970e-01 1.003e+00 0.98332 1.011
BMI 1.105e+00 9.049e-01 1.03152 1.184
CAD_history 6.391e-01 1.565e+00 0.34580 1.181
Stroke_history 1.451e+00 6.891e-01 0.86351 2.439
Peripheral.interv 1.712e+00 5.843e-01 0.92068 3.182
stenose0-49% 2.649e-07 3.775e+06 0.00000 Inf
stenose50-70% 5.400e-01 1.852e+00 0.05575 5.230
stenose70-90% 7.305e-01 1.369e+00 0.09839 5.424
stenose90-99% 6.625e-01 1.510e+00 0.08888 4.938
stenose100% (Occlusion) 1.457e+00 6.864e-01 0.08754 24.245
stenoseNA NA NA NA NA
stenose50-99% 2.262e-07 4.421e+06 0.00000 Inf
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
Concordance= 0.669 (se = 0.037 )
Likelihood ratio test= 24.74 on 19 df, p=0.2
Wald test = 22.97 on 19 df, p=0.2
Score (logrank) test = 24.39 on 19 df, p=0.2
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' IL6R_pg_ug_2015_rank ' and its association to ' epstroke.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epstroke.3years
Protein...................: IL6R_pg_ug_2015_rank
Effect size...............: 0.090975
Standard error............: 0.264899
Odds ratio (effect size)..: 1.095
Lower 95% CI..............: 0.652
Upper 95% CI..............: 1.841
T-value...................: 0.343431
P-value...................: 0.731274
Sample size in model......: 1006
Number of events..........: 61
> processing [MCP1_pg_ug_2015_rank]; 5 out of 5 proteins.
> cross tabulation of MCP1_pg_ug_2015_rank-stratum.
[-3.34148,0.00104) [ 0.00104,3.34148]
600 600
> fitting the model for MCP1_pg_ug_2015_rank-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose, data = TEMP.DF)
n= 1044, number of events= 62
(1344 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00104,3.34148] 1.163e-01 1.123e+00 2.615e-01 0.445 0.6566
Age 4.236e-02 1.043e+00 1.715e-02 2.470 0.0135 *
Gendermale 2.109e-01 1.235e+00 2.871e-01 0.734 0.4627
Hypertension.compositeno -1.076e-01 8.980e-01 4.149e-01 -0.259 0.7954
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes -4.084e-02 9.600e-01 3.103e-01 -0.132 0.8953
SmokerCurrentno -1.804e-01 8.349e-01 2.888e-01 -0.625 0.5322
SmokerCurrentyes NA NA 0.000e+00 NA NA
Med.Statin.LLDno 4.214e-01 1.524e+00 2.823e-01 1.493 0.1355
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 2.868e-01 1.332e+00 3.711e-01 0.773 0.4396
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD -5.616e-03 9.944e-01 6.832e-03 -0.822 0.4111
BMI 9.215e-02 1.097e+00 3.244e-02 2.841 0.0045 **
CAD_history -4.624e-01 6.298e-01 3.106e-01 -1.489 0.1366
Stroke_history 3.497e-01 1.419e+00 2.651e-01 1.319 0.1871
Peripheral.interv 4.865e-01 1.627e+00 3.152e-01 1.544 0.1227
stenose0-49% -1.547e+01 1.920e-07 3.676e+03 -0.004 0.9966
stenose50-70% -5.939e-01 5.522e-01 1.159e+00 -0.512 0.6085
stenose70-90% -2.858e-01 7.514e-01 1.025e+00 -0.279 0.7803
stenose90-99% -3.216e-01 7.250e-01 1.026e+00 -0.313 0.7541
stenose100% (Occlusion) 4.722e-01 1.604e+00 1.441e+00 0.328 0.7431
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% -1.528e+01 2.307e-07 3.934e+03 -0.004 0.9969
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00104,3.34148] 1.123e+00 8.902e-01 0.67282 1.875
Age 1.043e+00 9.585e-01 1.00878 1.079
Gendermale 1.235e+00 8.099e-01 0.70336 2.168
Hypertension.compositeno 8.980e-01 1.114e+00 0.39817 2.025
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 9.600e-01 1.042e+00 0.52254 1.764
SmokerCurrentno 8.349e-01 1.198e+00 0.47408 1.471
SmokerCurrentyes NA NA NA NA
Med.Statin.LLDno 1.524e+00 6.562e-01 0.87646 2.650
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 1.332e+00 7.507e-01 0.64369 2.757
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 9.944e-01 1.006e+00 0.98117 1.008
BMI 1.097e+00 9.120e-01 1.02898 1.169
CAD_history 6.298e-01 1.588e+00 0.34258 1.158
Stroke_history 1.419e+00 7.049e-01 0.84381 2.385
Peripheral.interv 1.627e+00 6.148e-01 0.87701 3.017
stenose0-49% 1.920e-07 5.208e+06 0.00000 Inf
stenose50-70% 5.522e-01 1.811e+00 0.05691 5.358
stenose70-90% 7.514e-01 1.331e+00 0.10089 5.597
stenose90-99% 7.250e-01 1.379e+00 0.09697 5.421
stenose100% (Occlusion) 1.604e+00 6.236e-01 0.09522 27.006
stenoseNA NA NA NA NA
stenose50-99% 2.307e-07 4.335e+06 0.00000 Inf
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
Concordance= 0.672 (se = 0.036 )
Likelihood ratio test= 25.52 on 19 df, p=0.1
Wald test = 23.76 on 19 df, p=0.2
Score (logrank) test = 25.03 on 19 df, p=0.2
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_pg_ug_2015_rank ' and its association to ' epstroke.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epstroke.3years
Protein...................: MCP1_pg_ug_2015_rank
Effect size...............: 0.116261
Standard error............: 0.261501
Odds ratio (effect size)..: 1.123
Lower 95% CI..............: 0.673
Upper 95% CI..............: 1.875
T-value...................: 0.444593
P-value...................: 0.6566139
Sample size in model......: 1044
Number of events..........: 62
* Analyzing the effect of plaque proteins on [epcoronary.3years].
- creating temporary SE for this work.
- making a 'Surv' object and adding this to temporary dataframe.
- making strata of each of the plaque proteins and start survival analysis.
> processing [IL6_rank]; 1 out of 5 proteins.
> cross tabulation of IL6_rank-stratum.
[-1.48329,0.00474) [ 0.00474,3.10694]
265 264
> fitting the model for IL6_rank-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose, data = TEMP.DF)
n= 477, number of events= 43
(1911 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00474,3.10694] 1.378e-01 1.148e+00 3.168e-01 0.435 0.6635
Age 4.680e-02 1.048e+00 2.288e-02 2.046 0.0408 *
Gendermale 8.433e-01 2.324e+00 4.519e-01 1.866 0.0620 .
Hypertension.compositeno -5.478e-01 5.782e-01 6.192e-01 -0.885 0.3763
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes 4.049e-01 1.499e+00 3.626e-01 1.117 0.2642
SmokerCurrentno -4.949e-01 6.097e-01 3.286e-01 -1.506 0.1321
SmokerCurrentyes NA NA 0.000e+00 NA NA
Med.Statin.LLDno 9.778e-02 1.103e+00 3.667e-01 0.267 0.7897
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 3.919e-01 1.480e+00 4.544e-01 0.862 0.3885
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD -7.231e-03 9.928e-01 8.598e-03 -0.841 0.4004
BMI 1.272e-02 1.013e+00 4.331e-02 0.294 0.7691
CAD_history 7.817e-01 2.185e+00 3.375e-01 2.316 0.0205 *
Stroke_history -2.712e-01 7.624e-01 3.572e-01 -0.759 0.4477
Peripheral.interv 3.803e-01 1.463e+00 3.575e-01 1.064 0.2874
stenose0-49% -8.465e-01 4.289e-01 8.354e+03 0.000 0.9999
stenose50-70% 1.550e+01 5.402e+06 4.119e+03 0.004 0.9970
stenose70-90% 1.589e+01 7.997e+06 4.119e+03 0.004 0.9969
stenose90-99% 1.577e+01 7.055e+06 4.119e+03 0.004 0.9969
stenose100% (Occlusion) NA NA 0.000e+00 NA NA
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% NA NA 0.000e+00 NA NA
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00474,3.10694] 1.148e+00 8.713e-01 0.6169 2.135
Age 1.048e+00 9.543e-01 1.0020 1.096
Gendermale 2.324e+00 4.303e-01 0.9584 5.635
Hypertension.compositeno 5.782e-01 1.729e+00 0.1718 1.946
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 1.499e+00 6.671e-01 0.7365 3.051
SmokerCurrentno 6.097e-01 1.640e+00 0.3201 1.161
SmokerCurrentyes NA NA NA NA
Med.Statin.LLDno 1.103e+00 9.068e-01 0.5375 2.262
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 1.480e+00 6.758e-01 0.6073 3.605
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 9.928e-01 1.007e+00 0.9762 1.010
BMI 1.013e+00 9.874e-01 0.9304 1.103
CAD_history 2.185e+00 4.576e-01 1.1278 4.234
Stroke_history 7.624e-01 1.312e+00 0.3786 1.536
Peripheral.interv 1.463e+00 6.837e-01 0.7259 2.947
stenose0-49% 4.289e-01 2.332e+00 0.0000 Inf
stenose50-70% 5.402e+06 1.851e-07 0.0000 Inf
stenose70-90% 7.997e+06 1.251e-07 0.0000 Inf
stenose90-99% 7.055e+06 1.417e-07 0.0000 Inf
stenose100% (Occlusion) NA NA NA NA
stenoseNA NA NA NA NA
stenose50-99% NA NA NA NA
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
Concordance= 0.749 (se = 0.033 )
Likelihood ratio test= 31.57 on 17 df, p=0.02
Wald test = 24.09 on 17 df, p=0.1
Score (logrank) test = 32.09 on 17 df, p=0.01
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' IL6_rank ' and its association to ' epcoronary.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epcoronary.3years
Protein...................: IL6_rank
Effect size...............: 0.137818
Standard error............: 0.316752
Odds ratio (effect size)..: 1.148
Lower 95% CI..............: 0.617
Upper 95% CI..............: 2.135
T-value...................: 0.435097
P-value...................: 0.6634919
Sample size in model......: 477
Number of events..........: 43
> processing [MCP1_rank]; 2 out of 5 proteins.
> cross tabulation of MCP1_rank-stratum.
[-2.41053,0.00444) [ 0.00444,3.12635]
283 282
> fitting the model for MCP1_rank-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose, data = TEMP.DF)
n= 509, number of events= 44
(1879 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00444,3.12635] 2.557e-01 1.291e+00 3.158e-01 0.810 0.4182
Age 3.929e-02 1.040e+00 2.294e-02 1.713 0.0868 .
Gendermale 6.478e-01 1.911e+00 4.234e-01 1.530 0.1260
Hypertension.compositeno -2.496e-01 7.791e-01 5.535e-01 -0.451 0.6520
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes 5.175e-01 1.678e+00 3.489e-01 1.483 0.1381
SmokerCurrentno -5.444e-01 5.802e-01 3.229e-01 -1.686 0.0918 .
SmokerCurrentyes NA NA 0.000e+00 NA NA
Med.Statin.LLDno 9.463e-02 1.099e+00 3.667e-01 0.258 0.7964
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 3.216e-01 1.379e+00 4.555e-01 0.706 0.4802
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD -1.149e-02 9.886e-01 8.315e-03 -1.382 0.1669
BMI 2.137e-02 1.022e+00 4.270e-02 0.500 0.6168
CAD_history 7.843e-01 2.191e+00 3.274e-01 2.395 0.0166 *
Stroke_history -1.394e-01 8.699e-01 3.382e-01 -0.412 0.6803
Peripheral.interv 5.109e-01 1.667e+00 3.537e-01 1.445 0.1486
stenose0-49% -8.090e-01 4.453e-01 9.205e+03 0.000 0.9999
stenose50-70% 1.566e+01 6.305e+06 5.158e+03 0.003 0.9976
stenose70-90% 1.598e+01 8.730e+06 5.158e+03 0.003 0.9975
stenose90-99% 1.591e+01 8.127e+06 5.158e+03 0.003 0.9975
stenose100% (Occlusion) NA NA 0.000e+00 NA NA
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% NA NA 0.000e+00 NA NA
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00444,3.12635] 1.291e+00 7.744e-01 0.6954 2.398
Age 1.040e+00 9.615e-01 0.9943 1.088
Gendermale 1.911e+00 5.232e-01 0.8335 4.383
Hypertension.compositeno 7.791e-01 1.284e+00 0.2633 2.306
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 1.678e+00 5.960e-01 0.8467 3.325
SmokerCurrentno 5.802e-01 1.724e+00 0.3081 1.092
SmokerCurrentyes NA NA NA NA
Med.Statin.LLDno 1.099e+00 9.097e-01 0.5357 2.256
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 1.379e+00 7.250e-01 0.5648 3.368
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 9.886e-01 1.012e+00 0.9726 1.005
BMI 1.022e+00 9.789e-01 0.9396 1.111
CAD_history 2.191e+00 4.565e-01 1.1532 4.162
Stroke_history 8.699e-01 1.150e+00 0.4483 1.688
Peripheral.interv 1.667e+00 6.000e-01 0.8333 3.334
stenose0-49% 4.453e-01 2.246e+00 0.0000 Inf
stenose50-70% 6.305e+06 1.586e-07 0.0000 Inf
stenose70-90% 8.730e+06 1.145e-07 0.0000 Inf
stenose90-99% 8.127e+06 1.230e-07 0.0000 Inf
stenose100% (Occlusion) NA NA NA NA
stenoseNA NA NA NA NA
stenose50-99% NA NA NA NA
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
Concordance= 0.731 (se = 0.036 )
Likelihood ratio test= 31 on 17 df, p=0.02
Wald test = 23.03 on 17 df, p=0.1
Score (logrank) test = 32.35 on 17 df, p=0.01
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_rank ' and its association to ' epcoronary.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epcoronary.3years
Protein...................: MCP1_rank
Effect size...............: 0.25569
Standard error............: 0.315832
Odds ratio (effect size)..: 1.291
Lower 95% CI..............: 0.695
Upper 95% CI..............: 2.398
T-value...................: 0.809576
P-value...................: 0.4181839
Sample size in model......: 509
Number of events..........: 44
> processing [IL6_pg_ug_2015_rank]; 3 out of 5 proteins.
> cross tabulation of IL6_pg_ug_2015_rank-stratum.
[-3.33061,0.00109) [ 0.00109,3.33061]
577 577
> fitting the model for IL6_pg_ug_2015_rank-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose, data = TEMP.DF)
n= 1002, number of events= 79
(1386 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00109,3.33061] 5.813e-02 1.060e+00 2.304e-01 0.252 0.80083
Age -3.043e-03 9.970e-01 1.510e-02 -0.201 0.84032
Gendermale 7.141e-01 2.042e+00 3.011e-01 2.372 0.01771 *
Hypertension.compositeno -7.882e-01 4.546e-01 5.252e-01 -1.501 0.13341
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes -1.803e-01 8.351e-01 2.782e-01 -0.648 0.51711
SmokerCurrentno -5.750e-01 5.627e-01 2.428e-01 -2.369 0.01785 *
SmokerCurrentyes NA NA 0.000e+00 NA NA
Med.Statin.LLDno 2.493e-01 1.283e+00 2.741e-01 0.909 0.36312
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 1.737e-01 1.190e+00 3.354e-01 0.518 0.60448
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD -2.040e-02 9.798e-01 5.967e-03 -3.419 0.00063 ***
BMI 1.169e-02 1.012e+00 3.338e-02 0.350 0.72620
CAD_history 9.403e-01 2.561e+00 2.410e-01 3.901 9.56e-05 ***
Stroke_history -1.024e-01 9.027e-01 2.526e-01 -0.405 0.68527
Peripheral.interv 4.301e-01 1.537e+00 2.613e-01 1.646 0.09979 .
stenose0-49% -1.581e+01 1.357e-07 3.608e+03 -0.004 0.99650
stenose50-70% -1.055e+00 3.483e-01 1.233e+00 -0.856 0.39220
stenose70-90% -1.716e-02 9.830e-01 1.022e+00 -0.017 0.98661
stenose90-99% -1.699e-01 8.437e-01 1.022e+00 -0.166 0.86794
stenose100% (Occlusion) -1.551e+01 1.841e-07 3.150e+03 -0.005 0.99607
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% 1.291e+00 3.637e+00 1.459e+00 0.885 0.37628
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00109,3.33061] 1.060e+00 9.435e-01 0.67470 1.6649
Age 9.970e-01 1.003e+00 0.96789 1.0269
Gendermale 2.042e+00 4.896e-01 1.13195 3.6847
Hypertension.compositeno 4.546e-01 2.200e+00 0.16241 1.2727
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 8.351e-01 1.198e+00 0.48403 1.4407
SmokerCurrentno 5.627e-01 1.777e+00 0.34966 0.9055
SmokerCurrentyes NA NA NA NA
Med.Statin.LLDno 1.283e+00 7.793e-01 0.74978 2.1959
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 1.190e+00 8.405e-01 0.61655 2.2958
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 9.798e-01 1.021e+00 0.96842 0.9913
BMI 1.012e+00 9.884e-01 0.94768 1.0802
CAD_history 2.561e+00 3.905e-01 1.59668 4.1070
Stroke_history 9.027e-01 1.108e+00 0.55019 1.4810
Peripheral.interv 1.537e+00 6.504e-01 0.92119 2.5661
stenose0-49% 1.357e-07 7.370e+06 0.00000 Inf
stenose50-70% 3.483e-01 2.871e+00 0.03108 3.9018
stenose70-90% 9.830e-01 1.017e+00 0.13256 7.2891
stenose90-99% 8.437e-01 1.185e+00 0.11388 6.2514
stenose100% (Occlusion) 1.841e-07 5.432e+06 0.00000 Inf
stenoseNA NA NA NA NA
stenose50-99% 3.637e+00 2.750e-01 0.20825 63.5179
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
Concordance= 0.738 (se = 0.027 )
Likelihood ratio test= 61.46 on 19 df, p=2e-06
Wald test = 60.06 on 19 df, p=4e-06
Score (logrank) test = 64.83 on 19 df, p=7e-07
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' IL6_pg_ug_2015_rank ' and its association to ' epcoronary.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epcoronary.3years
Protein...................: IL6_pg_ug_2015_rank
Effect size...............: 0.058129
Standard error............: 0.230422
Odds ratio (effect size)..: 1.06
Lower 95% CI..............: 0.675
Upper 95% CI..............: 1.665
T-value...................: 0.252272
P-value...................: 0.8008308
Sample size in model......: 1002
Number of events..........: 79
> processing [IL6R_pg_ug_2015_rank]; 4 out of 5 proteins.
> cross tabulation of IL6R_pg_ug_2015_rank-stratum.
[-3.33109,0.00108) [ 0.00108,3.33109]
578 578
> fitting the model for IL6R_pg_ug_2015_rank-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose, data = TEMP.DF)
n= 1006, number of events= 81
(1382 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00108,3.33109] 3.112e-01 1.365e+00 2.338e-01 1.331 0.183175
Age -9.543e-04 9.990e-01 1.485e-02 -0.064 0.948766
Gendermale 5.574e-01 1.746e+00 2.851e-01 1.955 0.050527 .
Hypertension.compositeno -8.059e-01 4.467e-01 5.248e-01 -1.536 0.124600
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes -1.867e-01 8.297e-01 2.766e-01 -0.675 0.499578
SmokerCurrentno -5.490e-01 5.775e-01 2.406e-01 -2.282 0.022506 *
SmokerCurrentyes NA NA 0.000e+00 NA NA
Med.Statin.LLDno 1.850e-01 1.203e+00 2.751e-01 0.673 0.501211
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 2.059e-01 1.229e+00 3.353e-01 0.614 0.539303
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD -2.060e-02 9.796e-01 5.884e-03 -3.501 0.000464 ***
BMI 7.733e-03 1.008e+00 3.365e-02 0.230 0.818252
CAD_history 9.287e-01 2.531e+00 2.382e-01 3.898 9.69e-05 ***
Stroke_history -1.891e-01 8.277e-01 2.508e-01 -0.754 0.450860
Peripheral.interv 3.230e-01 1.381e+00 2.580e-01 1.252 0.210705
stenose0-49% -1.552e+01 1.820e-07 3.417e+03 -0.005 0.996376
stenose50-70% -1.047e+00 3.509e-01 1.231e+00 -0.851 0.394713
stenose70-90% -1.764e-01 8.383e-01 1.023e+00 -0.172 0.863099
stenose90-99% -2.621e-01 7.694e-01 1.023e+00 -0.256 0.797760
stenose100% (Occlusion) -1.525e+01 2.383e-07 2.534e+03 -0.006 0.995199
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% 9.092e-01 2.482e+00 1.444e+00 0.629 0.529066
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00108,3.33109] 1.365e+00 7.325e-01 0.86324 2.1588
Age 9.990e-01 1.001e+00 0.97039 1.0286
Gendermale 1.746e+00 5.727e-01 0.99872 3.0529
Hypertension.compositeno 4.467e-01 2.239e+00 0.15970 1.2493
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 8.297e-01 1.205e+00 0.48248 1.4267
SmokerCurrentno 5.775e-01 1.732e+00 0.36039 0.9255
SmokerCurrentyes NA NA NA NA
Med.Statin.LLDno 1.203e+00 8.311e-01 0.70178 2.0630
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 1.229e+00 8.140e-01 0.63674 2.3705
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 9.796e-01 1.021e+00 0.96838 0.9910
BMI 1.008e+00 9.923e-01 0.94344 1.0765
CAD_history 2.531e+00 3.951e-01 1.58689 4.0375
Stroke_history 8.277e-01 1.208e+00 0.50632 1.3531
Peripheral.interv 1.381e+00 7.240e-01 0.83296 2.2903
stenose0-49% 1.820e-07 5.495e+06 0.00000 Inf
stenose50-70% 3.509e-01 2.850e+00 0.03145 3.9140
stenose70-90% 8.383e-01 1.193e+00 0.11291 6.2240
stenose90-99% 7.694e-01 1.300e+00 0.10364 5.7124
stenose100% (Occlusion) 2.383e-07 4.196e+06 0.00000 Inf
stenoseNA NA NA NA NA
stenose50-99% 2.482e+00 4.028e-01 0.14633 42.1104
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
Concordance= 0.73 (se = 0.029 )
Likelihood ratio test= 60.73 on 19 df, p=3e-06
Wald test = 60.13 on 19 df, p=4e-06
Score (logrank) test = 63.68 on 19 df, p=1e-06
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' IL6R_pg_ug_2015_rank ' and its association to ' epcoronary.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epcoronary.3years
Protein...................: IL6R_pg_ug_2015_rank
Effect size...............: 0.311249
Standard error............: 0.233838
Odds ratio (effect size)..: 1.365
Lower 95% CI..............: 0.863
Upper 95% CI..............: 2.159
T-value...................: 1.331043
P-value...................: 0.1831749
Sample size in model......: 1006
Number of events..........: 81
> processing [MCP1_pg_ug_2015_rank]; 5 out of 5 proteins.
> cross tabulation of MCP1_pg_ug_2015_rank-stratum.
[-3.34148,0.00104) [ 0.00104,3.34148]
600 600
> fitting the model for MCP1_pg_ug_2015_rank-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose, data = TEMP.DF)
n= 1044, number of events= 81
(1344 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00104,3.34148] -1.596e-02 9.842e-01 2.268e-01 -0.070 0.943890
Age -1.539e-03 9.985e-01 1.479e-02 -0.104 0.917138
Gendermale 6.124e-01 1.845e+00 2.852e-01 2.147 0.031760 *
Hypertension.compositeno -8.179e-01 4.414e-01 5.250e-01 -1.558 0.119285
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes -2.171e-01 8.048e-01 2.764e-01 -0.785 0.432187
SmokerCurrentno -5.707e-01 5.651e-01 2.391e-01 -2.387 0.016980 *
SmokerCurrentyes NA NA 0.000e+00 NA NA
Med.Statin.LLDno 2.564e-01 1.292e+00 2.724e-01 0.941 0.346513
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 1.932e-01 1.213e+00 3.346e-01 0.577 0.563740
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD -2.177e-02 9.785e-01 5.897e-03 -3.691 0.000223 ***
BMI 9.025e-03 1.009e+00 3.301e-02 0.273 0.784560
CAD_history 9.195e-01 2.508e+00 2.377e-01 3.868 0.000110 ***
Stroke_history -1.476e-01 8.628e-01 2.513e-01 -0.587 0.556941
Peripheral.interv 3.358e-01 1.399e+00 2.589e-01 1.297 0.194621
stenose0-49% -1.549e+01 1.880e-07 3.028e+03 -0.005 0.995919
stenose50-70% -1.074e+00 3.417e-01 1.233e+00 -0.871 0.383723
stenose70-90% -1.110e-01 8.949e-01 1.023e+00 -0.109 0.913589
stenose90-99% -1.822e-01 8.334e-01 1.023e+00 -0.178 0.858652
stenose100% (Occlusion) -1.520e+01 2.509e-07 2.633e+03 -0.006 0.995394
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% 1.014e+00 2.758e+00 1.440e+00 0.705 0.481061
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00104,3.34148] 9.842e-01 1.016e+00 0.6310 1.5351
Age 9.985e-01 1.002e+00 0.9699 1.0278
Gendermale 1.845e+00 5.420e-01 1.0549 3.2265
Hypertension.compositeno 4.414e-01 2.266e+00 0.1577 1.2351
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 8.048e-01 1.242e+00 0.4682 1.3835
SmokerCurrentno 5.651e-01 1.770e+00 0.3537 0.9029
SmokerCurrentyes NA NA NA NA
Med.Statin.LLDno 1.292e+00 7.738e-01 0.7577 2.2040
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 1.213e+00 8.244e-01 0.6296 2.3371
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 9.785e-01 1.022e+00 0.9672 0.9898
BMI 1.009e+00 9.910e-01 0.9458 1.0765
CAD_history 2.508e+00 3.987e-01 1.5740 3.9966
Stroke_history 8.628e-01 1.159e+00 0.5272 1.4119
Peripheral.interv 1.399e+00 7.147e-01 0.8423 2.3240
stenose0-49% 1.880e-07 5.318e+06 0.0000 Inf
stenose50-70% 3.417e-01 2.926e+00 0.0305 3.8279
stenose70-90% 8.949e-01 1.117e+00 0.1204 6.6502
stenose90-99% 8.334e-01 1.200e+00 0.1122 6.1898
stenose100% (Occlusion) 2.509e-07 3.986e+06 0.0000 Inf
stenoseNA NA NA NA NA
stenose50-99% 2.758e+00 3.626e-01 0.1641 46.3478
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
Concordance= 0.732 (se = 0.027 )
Likelihood ratio test= 60.47 on 19 df, p=3e-06
Wald test = 59.13 on 19 df, p=5e-06
Score (logrank) test = 62.13 on 19 df, p=2e-06
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_pg_ug_2015_rank ' and its association to ' epcoronary.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epcoronary.3years
Protein...................: MCP1_pg_ug_2015_rank
Effect size...............: -0.015964
Standard error............: 0.226816
Odds ratio (effect size)..: 0.984
Lower 95% CI..............: 0.631
Upper 95% CI..............: 1.535
T-value...................: -0.070382
P-value...................: 0.9438898
Sample size in model......: 1044
Number of events..........: 81
* Analyzing the effect of plaque proteins on [epcvdeath.3years].
- creating temporary SE for this work.
- making a 'Surv' object and adding this to temporary dataframe.
- making strata of each of the plaque proteins and start survival analysis.
> processing [IL6_rank]; 1 out of 5 proteins.
> cross tabulation of IL6_rank-stratum.
[-1.48329,0.00474) [ 0.00474,3.10694]
265 264
> fitting the model for IL6_rank-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose, data = TEMP.DF)
n= 477, number of events= 23
(1911 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00474,3.10694] -9.187e-02 9.122e-01 4.309e-01 -0.213 0.8312
Age 6.195e-02 1.064e+00 3.229e-02 1.919 0.0550 .
Gendermale 6.438e-01 1.904e+00 5.718e-01 1.126 0.2602
Hypertension.compositeno -1.812e+01 1.351e-08 4.520e+03 -0.004 0.9968
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes 6.838e-01 1.981e+00 5.048e-01 1.354 0.1756
SmokerCurrentno -7.494e-01 4.726e-01 4.470e-01 -1.677 0.0936 .
SmokerCurrentyes NA NA 0.000e+00 NA NA
Med.Statin.LLDno 6.669e-01 1.948e+00 4.697e-01 1.420 0.1556
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 5.143e-01 1.672e+00 6.467e-01 0.795 0.4265
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD -1.130e-02 9.888e-01 1.115e-02 -1.014 0.3107
BMI 3.610e-02 1.037e+00 5.620e-02 0.642 0.5207
CAD_history 5.531e-01 1.739e+00 4.562e-01 1.212 0.2254
Stroke_history 1.645e-01 1.179e+00 4.525e-01 0.364 0.7162
Peripheral.interv 2.184e-01 1.244e+00 5.444e-01 0.401 0.6882
stenose0-49% -5.590e-01 5.718e-01 3.107e+04 0.000 1.0000
stenose50-70% 4.197e-01 1.521e+00 1.643e+04 0.000 1.0000
stenose70-90% 1.810e+01 7.291e+07 1.408e+04 0.001 0.9990
stenose90-99% 1.800e+01 6.555e+07 1.408e+04 0.001 0.9990
stenose100% (Occlusion) NA NA 0.000e+00 NA NA
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% NA NA 0.000e+00 NA NA
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00474,3.10694] 9.122e-01 1.096e+00 0.3920 2.123
Age 1.064e+00 9.399e-01 0.9987 1.133
Gendermale 1.904e+00 5.253e-01 0.6207 5.839
Hypertension.compositeno 1.351e-08 7.403e+07 0.0000 Inf
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 1.981e+00 5.047e-01 0.7366 5.329
SmokerCurrentno 4.726e-01 2.116e+00 0.1968 1.135
SmokerCurrentyes NA NA NA NA
Med.Statin.LLDno 1.948e+00 5.133e-01 0.7760 4.892
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 1.672e+00 5.979e-01 0.4708 5.940
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 9.888e-01 1.011e+00 0.9674 1.011
BMI 1.037e+00 9.645e-01 0.9286 1.157
CAD_history 1.739e+00 5.752e-01 0.7110 4.251
Stroke_history 1.179e+00 8.483e-01 0.4856 2.861
Peripheral.interv 1.244e+00 8.038e-01 0.4280 3.616
stenose0-49% 5.718e-01 1.749e+00 0.0000 Inf
stenose50-70% 1.521e+00 6.573e-01 0.0000 Inf
stenose70-90% 7.291e+07 1.372e-08 0.0000 Inf
stenose90-99% 6.555e+07 1.526e-08 0.0000 Inf
stenose100% (Occlusion) NA NA NA NA
stenoseNA NA NA NA NA
stenose50-99% NA NA NA NA
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
Concordance= 0.791 (se = 0.041 )
Likelihood ratio test= 26.72 on 17 df, p=0.06
Wald test = 9.85 on 17 df, p=0.9
Score (logrank) test = 22.16 on 17 df, p=0.2
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' IL6_rank ' and its association to ' epcvdeath.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epcvdeath.3years
Protein...................: IL6_rank
Effect size...............: -0.091872
Standard error............: 0.430914
Odds ratio (effect size)..: 0.912
Lower 95% CI..............: 0.392
Upper 95% CI..............: 2.123
T-value...................: -0.213202
P-value...................: 0.8311691
Sample size in model......: 477
Number of events..........: 23
> processing [MCP1_rank]; 2 out of 5 proteins.
> cross tabulation of MCP1_rank-stratum.
[-2.41053,0.00444) [ 0.00444,3.12635]
283 282
> fitting the model for MCP1_rank-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose, data = TEMP.DF)
n= 509, number of events= 24
(1879 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00444,3.12635] -1.771e-01 8.377e-01 4.270e-01 -0.415 0.6783
Age 5.662e-02 1.058e+00 3.156e-02 1.794 0.0728 .
Gendermale 6.642e-01 1.943e+00 5.609e-01 1.184 0.2364
Hypertension.compositeno -1.815e+01 1.310e-08 4.307e+03 -0.004 0.9966
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes 6.356e-01 1.888e+00 5.042e-01 1.261 0.2075
SmokerCurrentno -8.101e-01 4.448e-01 4.374e-01 -1.852 0.0640 .
SmokerCurrentyes NA NA 0.000e+00 NA NA
Med.Statin.LLDno 6.893e-01 1.992e+00 4.646e-01 1.484 0.1379
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 2.638e-01 1.302e+00 6.552e-01 0.403 0.6872
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD -2.066e-02 9.796e-01 1.089e-02 -1.897 0.0578 .
BMI 1.251e-02 1.013e+00 5.815e-02 0.215 0.8296
CAD_history 2.554e-01 1.291e+00 4.455e-01 0.573 0.5665
Stroke_history 2.185e-01 1.244e+00 4.350e-01 0.502 0.6154
Peripheral.interv 5.211e-01 1.684e+00 5.060e-01 1.030 0.3031
stenose0-49% -7.104e-01 4.914e-01 3.570e+04 0.000 1.0000
stenose50-70% 3.764e-01 1.457e+00 2.096e+04 0.000 1.0000
stenose70-90% 1.826e+01 8.550e+07 1.922e+04 0.001 0.9992
stenose90-99% 1.805e+01 6.936e+07 1.922e+04 0.001 0.9993
stenose100% (Occlusion) NA NA 0.000e+00 NA NA
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% NA NA 0.000e+00 NA NA
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00444,3.12635] 8.377e-01 1.194e+00 0.3628 1.934
Age 1.058e+00 9.450e-01 0.9948 1.126
Gendermale 1.943e+00 5.147e-01 0.6472 5.833
Hypertension.compositeno 1.310e-08 7.634e+07 0.0000 Inf
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 1.888e+00 5.296e-01 0.7028 5.072
SmokerCurrentno 4.448e-01 2.248e+00 0.1887 1.048
SmokerCurrentyes NA NA NA NA
Med.Statin.LLDno 1.992e+00 5.019e-01 0.8015 4.953
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 1.302e+00 7.681e-01 0.3605 4.702
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 9.796e-01 1.021e+00 0.9589 1.001
BMI 1.013e+00 9.876e-01 0.9035 1.135
CAD_history 1.291e+00 7.746e-01 0.5391 3.091
Stroke_history 1.244e+00 8.037e-01 0.5305 2.918
Peripheral.interv 1.684e+00 5.939e-01 0.6246 4.540
stenose0-49% 4.914e-01 2.035e+00 0.0000 Inf
stenose50-70% 1.457e+00 6.863e-01 0.0000 Inf
stenose70-90% 8.550e+07 1.170e-08 0.0000 Inf
stenose90-99% 6.936e+07 1.442e-08 0.0000 Inf
stenose100% (Occlusion) NA NA NA NA
stenoseNA NA NA NA NA
stenose50-99% NA NA NA NA
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
Concordance= 0.801 (se = 0.04 )
Likelihood ratio test= 29.16 on 17 df, p=0.03
Wald test = 9.19 on 17 df, p=0.9
Score (logrank) test = 24.7 on 17 df, p=0.1
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_rank ' and its association to ' epcvdeath.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epcvdeath.3years
Protein...................: MCP1_rank
Effect size...............: -0.177103
Standard error............: 0.426996
Odds ratio (effect size)..: 0.838
Lower 95% CI..............: 0.363
Upper 95% CI..............: 1.934
T-value...................: -0.414764
P-value...................: 0.6783146
Sample size in model......: 509
Number of events..........: 24
> processing [IL6_pg_ug_2015_rank]; 3 out of 5 proteins.
> cross tabulation of IL6_pg_ug_2015_rank-stratum.
[-3.33061,0.00109) [ 0.00109,3.33061]
577 577
> fitting the model for IL6_pg_ug_2015_rank-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose, data = TEMP.DF)
n= 1002, number of events= 35
(1386 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00109,3.33061] 3.124e-01 1.367e+00 3.495e-01 0.894 0.371352
Age 6.928e-02 1.072e+00 2.589e-02 2.676 0.007450 **
Gendermale 1.013e+00 2.755e+00 4.933e-01 2.054 0.039942 *
Hypertension.compositeno -1.767e+01 2.123e-08 3.770e+03 -0.005 0.996261
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes -5.545e-02 9.461e-01 4.082e-01 -0.136 0.891958
SmokerCurrentno -4.117e-01 6.625e-01 3.828e-01 -1.076 0.282106
SmokerCurrentyes NA NA 0.000e+00 NA NA
Med.Statin.LLDno 3.902e-02 1.040e+00 4.239e-01 0.092 0.926647
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 1.096e+00 2.991e+00 3.963e-01 2.765 0.005696 **
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD -3.121e-02 9.693e-01 9.102e-03 -3.428 0.000607 ***
BMI 6.702e-02 1.069e+00 5.147e-02 1.302 0.192850
CAD_history 2.453e-01 1.278e+00 3.548e-01 0.692 0.489238
Stroke_history -1.405e-01 8.689e-01 3.867e-01 -0.363 0.716259
Peripheral.interv 6.223e-01 1.863e+00 4.073e-01 1.528 0.126503
stenose0-49% -1.957e+01 3.162e-09 2.801e+04 -0.001 0.999442
stenose50-70% -8.921e-01 4.098e-01 1.238e+00 -0.720 0.471253
stenose70-90% -1.380e+00 2.515e-01 1.065e+00 -1.296 0.195019
stenose90-99% -8.273e-01 4.372e-01 1.042e+00 -0.794 0.427146
stenose100% (Occlusion) -1.912e+01 4.954e-09 1.969e+04 -0.001 0.999225
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% -1.913e+01 4.932e-09 4.806e+04 0.000 0.999682
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00109,3.33061] 1.367e+00 7.317e-01 0.68899 2.7110
Age 1.072e+00 9.331e-01 1.01871 1.1275
Gendermale 2.755e+00 3.630e-01 1.04766 7.2440
Hypertension.compositeno 2.123e-08 4.709e+07 0.00000 Inf
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 9.461e-01 1.057e+00 0.42503 2.1058
SmokerCurrentno 6.625e-01 1.509e+00 0.31288 1.4029
SmokerCurrentyes NA NA NA NA
Med.Statin.LLDno 1.040e+00 9.617e-01 0.45305 2.3865
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 2.991e+00 3.343e-01 1.37565 6.5034
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 9.693e-01 1.032e+00 0.95214 0.9867
BMI 1.069e+00 9.352e-01 0.96671 1.1828
CAD_history 1.278e+00 7.824e-01 0.63761 2.5618
Stroke_history 8.689e-01 1.151e+00 0.40722 1.8539
Peripheral.interv 1.863e+00 5.367e-01 0.83869 4.1396
stenose0-49% 3.162e-09 3.162e+08 0.00000 Inf
stenose50-70% 4.098e-01 2.440e+00 0.03619 4.6407
stenose70-90% 2.515e-01 3.976e+00 0.03118 2.0286
stenose90-99% 4.372e-01 2.287e+00 0.05675 3.3690
stenose100% (Occlusion) 4.954e-09 2.018e+08 0.00000 Inf
stenoseNA NA NA NA NA
stenose50-99% 4.932e-09 2.028e+08 0.00000 Inf
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
Concordance= 0.841 (se = 0.028 )
Likelihood ratio test= 59.89 on 19 df, p=4e-06
Wald test = 21.5 on 19 df, p=0.3
Score (logrank) test = 56.6 on 19 df, p=1e-05
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' IL6_pg_ug_2015_rank ' and its association to ' epcvdeath.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epcvdeath.3years
Protein...................: IL6_pg_ug_2015_rank
Effect size...............: 0.312398
Standard error............: 0.349461
Odds ratio (effect size)..: 1.367
Lower 95% CI..............: 0.689
Upper 95% CI..............: 2.711
T-value...................: 0.893944
P-value...................: 0.3713521
Sample size in model......: 1002
Number of events..........: 35
> processing [IL6R_pg_ug_2015_rank]; 4 out of 5 proteins.
> cross tabulation of IL6R_pg_ug_2015_rank-stratum.
[-3.33109,0.00108) [ 0.00108,3.33109]
578 578
> fitting the model for IL6R_pg_ug_2015_rank-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose, data = TEMP.DF)
n= 1006, number of events= 35
(1382 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00108,3.33109] 5.464e-01 1.727e+00 3.680e-01 1.485 0.137593
Age 6.965e-02 1.072e+00 2.601e-02 2.677 0.007422 **
Gendermale 1.005e+00 2.732e+00 4.908e-01 2.048 0.040545 *
Hypertension.compositeno -1.772e+01 2.009e-08 3.857e+03 -0.005 0.996334
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes 4.218e-02 1.043e+00 4.083e-01 0.103 0.917718
SmokerCurrentno -3.826e-01 6.821e-01 3.836e-01 -0.997 0.318610
SmokerCurrentyes NA NA 0.000e+00 NA NA
Med.Statin.LLDno -3.352e-02 9.670e-01 4.260e-01 -0.079 0.937271
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 1.105e+00 3.019e+00 3.963e-01 2.788 0.005308 **
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD -3.056e-02 9.699e-01 9.111e-03 -3.354 0.000795 ***
BMI 6.759e-02 1.070e+00 5.231e-02 1.292 0.196361
CAD_history 2.611e-01 1.298e+00 3.523e-01 0.741 0.458589
Stroke_history -1.680e-01 8.454e-01 3.863e-01 -0.435 0.663620
Peripheral.interv 5.397e-01 1.716e+00 4.005e-01 1.348 0.177778
stenose0-49% -1.933e+01 4.012e-09 3.424e+04 -0.001 0.999549
stenose50-70% -9.530e-01 3.856e-01 1.235e+00 -0.772 0.440403
stenose70-90% -1.527e+00 2.172e-01 1.063e+00 -1.437 0.150749
stenose90-99% -1.042e+00 3.528e-01 1.045e+00 -0.997 0.318684
stenose100% (Occlusion) -1.934e+01 3.981e-09 2.180e+04 -0.001 0.999292
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% -1.899e+01 5.679e-09 3.413e+04 -0.001 0.999556
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00108,3.33109] 1.727e+00 5.790e-01 0.83957 3.5528
Age 1.072e+00 9.327e-01 1.01884 1.1282
Gendermale 2.732e+00 3.660e-01 1.04423 7.1500
Hypertension.compositeno 2.009e-08 4.978e+07 0.00000 Inf
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 1.043e+00 9.587e-01 0.46857 2.3220
SmokerCurrentno 6.821e-01 1.466e+00 0.32163 1.4467
SmokerCurrentyes NA NA NA NA
Med.Statin.LLDno 9.670e-01 1.034e+00 0.41961 2.2286
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 3.019e+00 3.313e-01 1.38825 6.5635
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 9.699e-01 1.031e+00 0.95273 0.9874
BMI 1.070e+00 9.346e-01 0.96566 1.1854
CAD_history 1.298e+00 7.702e-01 0.65089 2.5901
Stroke_history 8.454e-01 1.183e+00 0.39652 1.8023
Peripheral.interv 1.716e+00 5.829e-01 0.78252 3.7609
stenose0-49% 4.012e-09 2.493e+08 0.00000 Inf
stenose50-70% 3.856e-01 2.593e+00 0.03426 4.3404
stenose70-90% 2.172e-01 4.604e+00 0.02706 1.7434
stenose90-99% 3.528e-01 2.835e+00 0.04551 2.7347
stenose100% (Occlusion) 3.981e-09 2.512e+08 0.00000 Inf
stenoseNA NA NA NA NA
stenose50-99% 5.679e-09 1.761e+08 0.00000 Inf
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
Concordance= 0.839 (se = 0.03 )
Likelihood ratio test= 61.36 on 19 df, p=2e-06
Wald test = 24.03 on 19 df, p=0.2
Score (logrank) test = 59.66 on 19 df, p=4e-06
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' IL6R_pg_ug_2015_rank ' and its association to ' epcvdeath.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epcvdeath.3years
Protein...................: IL6R_pg_ug_2015_rank
Effect size...............: 0.546431
Standard error............: 0.368013
Odds ratio (effect size)..: 1.727
Lower 95% CI..............: 0.84
Upper 95% CI..............: 3.553
T-value...................: 1.484815
P-value...................: 0.1375929
Sample size in model......: 1006
Number of events..........: 35
> processing [MCP1_pg_ug_2015_rank]; 5 out of 5 proteins.
> cross tabulation of MCP1_pg_ug_2015_rank-stratum.
[-3.34148,0.00104) [ 0.00104,3.34148]
600 600
> fitting the model for MCP1_pg_ug_2015_rank-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose, data = TEMP.DF)
n= 1044, number of events= 35
(1344 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00104,3.34148] -2.174e-02 9.785e-01 3.452e-01 -0.063 0.949793
Age 6.789e-02 1.070e+00 2.577e-02 2.635 0.008421 **
Gendermale 1.041e+00 2.832e+00 4.920e-01 2.116 0.034334 *
Hypertension.compositeno -1.769e+01 2.075e-08 3.765e+03 -0.005 0.996251
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes -2.627e-02 9.741e-01 4.067e-01 -0.065 0.948492
SmokerCurrentno -4.254e-01 6.535e-01 3.816e-01 -1.115 0.264940
SmokerCurrentyes NA NA 0.000e+00 NA NA
Med.Statin.LLDno 8.787e-02 1.092e+00 4.219e-01 0.208 0.834997
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 1.088e+00 2.970e+00 3.978e-01 2.736 0.006214 **
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD -3.243e-02 9.681e-01 9.119e-03 -3.556 0.000376 ***
BMI 6.918e-02 1.072e+00 5.133e-02 1.348 0.177742
CAD_history 2.495e-01 1.283e+00 3.531e-01 0.707 0.479796
Stroke_history -1.160e-01 8.905e-01 3.884e-01 -0.299 0.765236
Peripheral.interv 5.734e-01 1.774e+00 4.037e-01 1.421 0.155458
stenose0-49% -1.972e+01 2.716e-09 2.937e+04 -0.001 0.999464
stenose50-70% -9.727e-01 3.781e-01 1.239e+00 -0.785 0.432437
stenose70-90% -1.445e+00 2.358e-01 1.068e+00 -1.353 0.176192
stenose90-99% -9.011e-01 4.061e-01 1.048e+00 -0.860 0.389738
stenose100% (Occlusion) -1.926e+01 4.335e-09 2.009e+04 -0.001 0.999235
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% -1.904e+01 5.407e-09 3.419e+04 -0.001 0.999556
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00104,3.34148] 9.785e-01 1.022e+00 0.49743 1.9248
Age 1.070e+00 9.344e-01 1.01754 1.1257
Gendermale 2.832e+00 3.531e-01 1.07986 7.4287
Hypertension.compositeno 2.075e-08 4.820e+07 0.00000 Inf
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 9.741e-01 1.027e+00 0.43893 2.1616
SmokerCurrentno 6.535e-01 1.530e+00 0.30931 1.3806
SmokerCurrentyes NA NA NA NA
Med.Statin.LLDno 1.092e+00 9.159e-01 0.47761 2.4961
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 2.970e+00 3.368e-01 1.36177 6.4754
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 9.681e-01 1.033e+00 0.95094 0.9855
BMI 1.072e+00 9.332e-01 0.96906 1.1851
CAD_history 1.283e+00 7.792e-01 0.64242 2.5638
Stroke_history 8.905e-01 1.123e+00 0.41589 1.9066
Peripheral.interv 1.774e+00 5.636e-01 0.80432 3.9142
stenose0-49% 2.716e-09 3.681e+08 0.00000 Inf
stenose50-70% 3.781e-01 2.645e+00 0.03333 4.2880
stenose70-90% 2.358e-01 4.241e+00 0.02906 1.9132
stenose90-99% 4.061e-01 2.462e+00 0.05211 3.1654
stenose100% (Occlusion) 4.335e-09 2.307e+08 0.00000 Inf
stenoseNA NA NA NA NA
stenose50-99% 5.407e-09 1.849e+08 0.00000 Inf
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
Concordance= 0.839 (se = 0.029 )
Likelihood ratio test= 60.04 on 19 df, p=4e-06
Wald test = 21.96 on 19 df, p=0.3
Score (logrank) test = 56.98 on 19 df, p=1e-05
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_pg_ug_2015_rank ' and its association to ' epcvdeath.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epcvdeath.3years
Protein...................: MCP1_pg_ug_2015_rank
Effect size...............: -0.021735
Standard error............: 0.345188
Odds ratio (effect size)..: 0.978
Lower 95% CI..............: 0.497
Upper 95% CI..............: 1.925
T-value...................: -0.062967
P-value...................: 0.9497931
Sample size in model......: 1044
Number of events..........: 35 
cat("- Edit the column names...\n")- Edit the column names...colnames(COX.results) = c("Dataset", "Outcome", "CpG",
"Beta", "s.e.m.",
"HR", "low95CI", "up95CI",
"Z-value", "P-value", "SampleSize", "N_events")
cat("- Correct the variable types...\n")- Correct the variable types...COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)
AEDB.CEA.COX.results <- COX.results
# Save the data
cat("- Writing results to Excel-file...\n")- Writing results to Excel-file...head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AEDB.CEA.COX.results,
file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Cox.2G.MODEL2.xlsx"),
creator = "Sander W. van der Laan",
sheetName = "Results", headerStyle = head.style,
row.names = FALSE, col.names = TRUE, overwrite = TRUE)
# Removing intermediates
cat("- Removing intermediate files...\n")- Removing intermediate files...rm(TEMP.DF, protein, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AEDB.CEA.COX.results)
rm(head.style)object 'head.style' not foundMODEL 3
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))
# Looping over each protein/endpoint/time combination
for (i in 1:length(times)){
eptime = times[i]
ep = endpoints[i]
cat(paste0("* Analyzing the effect of plaque proteins on [",ep,"].\n"))
cat(" - creating temporary SE for this work.\n")
TEMP.DF = as.data.frame(AEDB.CEA)
cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
TEMP.DF$event <- as.integer(TEMP.DF[,ep])
#as.integer(TEMP.DF[,ep] == "Excluded")
TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
cat(" - making strata of each of the plaque proteins and start survival analysis.\n")
for (protein in 1:length(TRAITS.PROTEIN.RANK)){
cat(paste0(" > processing [",TRAITS.PROTEIN.RANK[protein],"]; ",protein," out of ",length(TRAITS.PROTEIN.RANK)," proteins.\n"))
# splitting into two groups
TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]] <- cut2(TEMP.DF[,TRAITS.PROTEIN.RANK[protein]], g = 2)
cat(paste0(" > cross tabulation of ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
show(table(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]))
cat(paste0("\n > fitting the model for ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
fit <- survfit(as.formula(paste0("y ~ ", TRAITS.PROTEIN.RANK[protein])), data = TEMP.DF)
cat(paste0("\n > make a Kaplan-Meier-shizzle...\n"))
# make Kaplan-Meier curve and save it
show(ggsurvplot(fit, data = TEMP.DF,
palette = c("#DB003F", "#1290D9"),
# palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
linetype = c(1,2),
# linetype = c(1,2,3,4),
# conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
pval = FALSE, pval.method = FALSE, pval.size = 4,
risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
censor = FALSE,
legend = "right",
legend.title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
legend.labs = c("low", "high"),
title = paste0("Risk of ",ep,""), xlab = "Time [years]", font.main = c(16, "bold", "black")))
dev.copy2pdf(file = paste0(COX_loc,"/",
Today,".AEDB.CEA.survival.",ep,".2G.",
TRAITS.PROTEIN.RANK[protein],".pdf"), width = 12, height = 10, onefile = FALSE)
cat(paste0("\n > perform the Cox-regression fashizzle and plot it...\n"))
### Do Cox-regression and plot it
### MODEL 3 same to model 2, with additional adjustments for circulating CRP levels
cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]+Age + Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + CAD_history + Stroke_history + Peripheral.interv + stenose + hsCRP_plasma, data = TEMP.DF)
coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]])+Age + Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + CAD_history + Stroke_history + Peripheral.interv + stenose + hsCRP_plasma, data = TEMP.DF)
plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
# ylim = c(0.2, 1), xlim = c(0,3), col = c("#595A5C", "#DB003F", "#1290D9"),
ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
lty = c(1,2), lwd = 2,
ylab = "Suvival probability", xlab = "FU time [years]",
mark.time = FALSE, axes = FALSE, bty = "n")
legend("topright",
c("low", "high"),
title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
col = c("#DB003F", "#1290D9"),
lty = c(1,2), lwd = 2,
bty = "n")
axis(side = 1, at = seq(0, 3, by = 1))
axis(side = 2, at = seq(0, 1, by = 0.2))
dev.copy2pdf(file = paste0(COX_loc,"/",
Today,".AEDB.CEA.Cox.",ep,".2G.",
# Today,".AEDB.CEA.Cox.",ep,".4G.",
TRAITS.PROTEIN.RANK[protein],".MODEL3.pdf"), height = 12, width = 10, onefile = TRUE)
show(summary(cox))
cat(paste0("\n > writing the Cox-regression fashizzle to Excel...\n"))
COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
COX.results.TEMP[1,] = COX.STAT(cox, "AEDB.CEA", ep, TRAITS.PROTEIN.RANK[protein])
COX.results = rbind(COX.results, COX.results.TEMP)
}
}* Analyzing the effect of plaque proteins on [epmajor.3years].
- creating temporary SE for this work.
- making a 'Surv' object and adding this to temporary dataframe.
- making strata of each of the plaque proteins and start survival analysis.
> processing [IL6_rank]; 1 out of 5 proteins.
> cross tabulation of IL6_rank-stratum.
[-1.48329,0.00474) [ 0.00474,3.10694]
265 264
> fitting the model for IL6_rank-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + hsCRP_plasma, data = TEMP.DF)
n= 268, number of events= 35
(2120 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00474,3.10694] -5.459e-01 5.793e-01 3.897e-01 -1.401 0.16128
Age 1.227e-01 1.131e+00 3.092e-02 3.969 7.23e-05 ***
Gendermale 1.010e+00 2.746e+00 5.246e-01 1.926 0.05415 .
Hypertension.compositeno -4.907e-01 6.122e-01 6.472e-01 -0.758 0.44835
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes 1.211e+00 3.355e+00 4.305e-01 2.812 0.00492 **
SmokerCurrentno -5.491e-01 5.775e-01 3.795e-01 -1.447 0.14788
SmokerCurrentyes NA NA 0.000e+00 NA NA
Med.Statin.LLDno 7.838e-02 1.082e+00 4.133e-01 0.190 0.84961
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno -7.814e-01 4.578e-01 8.461e-01 -0.924 0.35571
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD 7.143e-03 1.007e+00 1.087e-02 0.657 0.51118
BMI 1.431e-03 1.001e+00 5.364e-02 0.027 0.97871
CAD_history 5.748e-01 1.777e+00 3.866e-01 1.487 0.13705
Stroke_history -1.171e-01 8.895e-01 4.013e-01 -0.292 0.77037
Peripheral.interv 1.034e-01 1.109e+00 4.582e-01 0.226 0.82151
stenose0-49% -1.938e+01 3.824e-09 1.012e+04 -0.002 0.99847
stenose50-70% -1.844e+01 9.795e-09 3.557e+03 -0.005 0.99586
stenose70-90% -2.610e+00 7.353e-02 1.186e+00 -2.201 0.02774 *
stenose90-99% -3.683e+00 2.514e-02 1.248e+00 -2.950 0.00317 **
stenose100% (Occlusion) NA NA 0.000e+00 NA NA
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% NA NA 0.000e+00 NA NA
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
hsCRP_plasma 5.765e-03 1.006e+00 1.816e-03 3.175 0.00150 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00474,3.10694] 5.793e-01 1.726e+00 0.269928 1.2435
Age 1.131e+00 8.845e-01 1.064087 1.2012
Gendermale 2.746e+00 3.642e-01 0.982133 7.6770
Hypertension.compositeno 6.122e-01 1.633e+00 0.172207 2.1766
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 3.355e+00 2.980e-01 1.443127 7.8007
SmokerCurrentno 5.775e-01 1.732e+00 0.274482 1.2149
SmokerCurrentyes NA NA NA NA
Med.Statin.LLDno 1.082e+00 9.246e-01 0.481055 2.4315
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 4.578e-01 2.185e+00 0.087191 2.4033
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 1.007e+00 9.929e-01 0.985934 1.0289
BMI 1.001e+00 9.986e-01 0.901503 1.1124
CAD_history 1.777e+00 5.628e-01 0.832850 3.7908
Stroke_history 8.895e-01 1.124e+00 0.405065 1.9531
Peripheral.interv 1.109e+00 9.018e-01 0.451705 2.7223
stenose0-49% 3.824e-09 2.615e+08 0.000000 Inf
stenose50-70% 9.795e-09 1.021e+08 0.000000 Inf
stenose70-90% 7.353e-02 1.360e+01 0.007196 0.7514
stenose90-99% 2.514e-02 3.978e+01 0.002176 0.2904
stenose100% (Occlusion) NA NA NA NA
stenoseNA NA NA NA NA
stenose50-99% NA NA NA NA
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
hsCRP_plasma 1.006e+00 9.943e-01 1.002209 1.0094
Concordance= 0.811 (se = 0.035 )
Likelihood ratio test= 57.83 on 18 df, p=5e-06
Wald test = 36.37 on 18 df, p=0.006
Score (logrank) test = 99.38 on 18 df, p=3e-13
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' IL6_rank ' and its association to ' epmajor.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epmajor.3years
Protein...................: IL6_rank
Effect size...............: -0.545852
Standard error............: 0.389675
Odds ratio (effect size)..: 0.579
Lower 95% CI..............: 0.27
Upper 95% CI..............: 1.243
T-value...................: -1.400788
P-value...................: 0.1612775
Sample size in model......: 268
Number of events..........: 35
> processing [MCP1_rank]; 2 out of 5 proteins.
> cross tabulation of MCP1_rank-stratum.
[-2.41053,0.00444) [ 0.00444,3.12635]
283 282
> fitting the model for MCP1_rank-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + hsCRP_plasma, data = TEMP.DF)
n= 282, number of events= 37
(2106 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00444,3.12635] -8.868e-01 4.120e-01 3.678e-01 -2.411 0.015921 *
Age 1.015e-01 1.107e+00 2.855e-02 3.555 0.000377 ***
Gendermale 1.123e+00 3.073e+00 5.091e-01 2.205 0.027422 *
Hypertension.compositeno -4.126e-01 6.619e-01 6.362e-01 -0.649 0.516564
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes 7.775e-01 2.176e+00 4.208e-01 1.848 0.064627 .
SmokerCurrentno -6.027e-01 5.474e-01 3.683e-01 -1.636 0.101762
SmokerCurrentyes NA NA 0.000e+00 NA NA
Med.Statin.LLDno 3.050e-02 1.031e+00 4.058e-01 0.075 0.940097
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno -1.373e+00 2.532e-01 8.302e-01 -1.654 0.098072 .
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD 6.310e-04 1.001e+00 1.018e-02 0.062 0.950582
BMI 1.886e-02 1.019e+00 4.571e-02 0.412 0.679994
CAD_history 3.407e-01 1.406e+00 3.736e-01 0.912 0.361863
Stroke_history -2.990e-01 7.415e-01 3.852e-01 -0.776 0.437536
Peripheral.interv 2.667e-01 1.306e+00 4.307e-01 0.619 0.535753
stenose0-49% -2.044e+01 1.330e-09 1.406e+04 -0.001 0.998840
stenose50-70% -1.946e+01 3.552e-09 5.496e+03 -0.004 0.997175
stenose70-90% -2.985e+00 5.053e-02 1.196e+00 -2.495 0.012581 *
stenose90-99% -3.958e+00 1.911e-02 1.243e+00 -3.185 0.001448 **
stenose100% (Occlusion) NA NA 0.000e+00 NA NA
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% NA NA 0.000e+00 NA NA
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
hsCRP_plasma 6.384e-03 1.006e+00 1.939e-03 3.293 0.000991 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00444,3.12635] 4.120e-01 2.427e+00 0.200342 0.8472
Age 1.107e+00 9.035e-01 1.046604 1.1705
Gendermale 3.073e+00 3.254e-01 1.133122 8.3358
Hypertension.compositeno 6.619e-01 1.511e+00 0.190237 2.3030
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 2.176e+00 4.595e-01 0.953914 4.9640
SmokerCurrentno 5.474e-01 1.827e+00 0.265933 1.1266
SmokerCurrentyes NA NA NA NA
Med.Statin.LLDno 1.031e+00 9.700e-01 0.465386 2.2839
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 2.532e-01 3.949e+00 0.049753 1.2889
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 1.001e+00 9.994e-01 0.980861 1.0208
BMI 1.019e+00 9.813e-01 0.931702 1.1146
CAD_history 1.406e+00 7.113e-01 0.675969 2.9240
Stroke_history 7.415e-01 1.349e+00 0.348535 1.5776
Peripheral.interv 1.306e+00 7.659e-01 0.561294 3.0374
stenose0-49% 1.330e-09 7.519e+08 0.000000 Inf
stenose50-70% 3.552e-09 2.815e+08 0.000000 Inf
stenose70-90% 5.053e-02 1.979e+01 0.004845 0.5270
stenose90-99% 1.911e-02 5.234e+01 0.001673 0.2182
stenose100% (Occlusion) NA NA NA NA
stenoseNA NA NA NA NA
stenose50-99% NA NA NA NA
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
hsCRP_plasma 1.006e+00 9.936e-01 1.002588 1.0102
Concordance= 0.785 (se = 0.039 )
Likelihood ratio test= 56.22 on 18 df, p=8e-06
Wald test = 36.47 on 18 df, p=0.006
Score (logrank) test = 96.15 on 18 df, p=1e-12
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_rank ' and its association to ' epmajor.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epmajor.3years
Protein...................: MCP1_rank
Effect size...............: -0.886767
Standard error............: 0.367844
Odds ratio (effect size)..: 0.412
Lower 95% CI..............: 0.2
Upper 95% CI..............: 0.847
T-value...................: -2.410713
P-value...................: 0.01592137
Sample size in model......: 282
Number of events..........: 37
> processing [IL6_pg_ug_2015_rank]; 3 out of 5 proteins.
> cross tabulation of IL6_pg_ug_2015_rank-stratum.
[-3.33061,0.00109) [ 0.00109,3.33061]
577 577
> fitting the model for IL6_pg_ug_2015_rank-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + hsCRP_plasma, data = TEMP.DF)
n= 618, number of events= 70
(1770 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00109,3.33061] 3.142e-01 1.369e+00 2.485e-01 1.264 0.206167
Age 6.558e-02 1.068e+00 1.756e-02 3.735 0.000188 ***
Gendermale 1.155e+00 3.173e+00 3.397e-01 3.399 0.000676 ***
Hypertension.compositeno -1.540e-01 8.573e-01 4.184e-01 -0.368 0.712809
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes 1.634e-01 1.177e+00 2.902e-01 0.563 0.573433
SmokerCurrentno -4.001e-01 6.702e-01 2.676e-01 -1.495 0.134858
SmokerCurrentyes NA NA 0.000e+00 NA NA
Med.Statin.LLDno 3.136e-01 1.368e+00 2.750e-01 1.141 0.254056
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 2.109e-01 1.235e+00 3.565e-01 0.592 0.554076
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD -1.227e-02 9.878e-01 6.612e-03 -1.856 0.063401 .
BMI 3.692e-02 1.038e+00 3.542e-02 1.042 0.297219
CAD_history 2.888e-03 1.003e+00 2.698e-01 0.011 0.991459
Stroke_history -1.418e-01 8.678e-01 2.673e-01 -0.531 0.595723
Peripheral.interv 8.442e-01 2.326e+00 2.774e-01 3.043 0.002344 **
stenose0-49% -1.473e+01 3.992e-07 2.091e+03 -0.007 0.994378
stenose50-70% -1.207e+00 2.991e-01 1.241e+00 -0.973 0.330584
stenose70-90% -3.726e-01 6.889e-01 1.031e+00 -0.361 0.717882
stenose90-99% -5.464e-01 5.790e-01 1.033e+00 -0.529 0.596974
stenose100% (Occlusion) 1.423e+00 4.148e+00 1.474e+00 0.965 0.334614
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% NA NA 0.000e+00 NA NA
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
hsCRP_plasma 1.720e-03 1.002e+00 6.275e-04 2.741 0.006131 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00109,3.33061] 1.369e+00 7.304e-01 0.84119 2.229
Age 1.068e+00 9.365e-01 1.03166 1.105
Gendermale 3.173e+00 3.151e-01 1.63057 6.175
Hypertension.compositeno 8.573e-01 1.167e+00 0.37752 1.947
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 1.177e+00 8.493e-01 0.66674 2.079
SmokerCurrentno 6.702e-01 1.492e+00 0.39667 1.132
SmokerCurrentyes NA NA NA NA
Med.Statin.LLDno 1.368e+00 7.308e-01 0.79826 2.346
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 1.235e+00 8.098e-01 0.61397 2.484
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 9.878e-01 1.012e+00 0.97508 1.001
BMI 1.038e+00 9.638e-01 0.96803 1.112
CAD_history 1.003e+00 9.971e-01 0.59104 1.702
Stroke_history 8.678e-01 1.152e+00 0.51388 1.465
Peripheral.interv 2.326e+00 4.299e-01 1.35042 4.007
stenose0-49% 3.992e-07 2.505e+06 0.00000 Inf
stenose50-70% 2.991e-01 3.343e+00 0.02629 3.402
stenose70-90% 6.889e-01 1.452e+00 0.09126 5.201
stenose90-99% 5.790e-01 1.727e+00 0.07641 4.388
stenose100% (Occlusion) 4.148e+00 2.411e-01 0.23058 74.614
stenoseNA NA NA NA NA
stenose50-99% NA NA NA NA
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
hsCRP_plasma 1.002e+00 9.983e-01 1.00049 1.003
Concordance= 0.72 (se = 0.032 )
Likelihood ratio test= 53.45 on 19 df, p=4e-05
Wald test = 44.44 on 19 df, p=8e-04
Score (logrank) test = 49.32 on 19 df, p=2e-04
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' IL6_pg_ug_2015_rank ' and its association to ' epmajor.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epmajor.3years
Protein...................: IL6_pg_ug_2015_rank
Effect size...............: 0.314211
Standard error............: 0.24855
Odds ratio (effect size)..: 1.369
Lower 95% CI..............: 0.841
Upper 95% CI..............: 2.229
T-value...................: 1.264176
P-value...................: 0.2061668
Sample size in model......: 618
Number of events..........: 70
> processing [IL6R_pg_ug_2015_rank]; 4 out of 5 proteins.
> cross tabulation of IL6R_pg_ug_2015_rank-stratum.
[-3.33109,0.00108) [ 0.00108,3.33109]
578 578
> fitting the model for IL6R_pg_ug_2015_rank-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + hsCRP_plasma, data = TEMP.DF)
n= 623, number of events= 73
(1765 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00108,3.33109] 4.692e-01 1.599e+00 2.551e-01 1.839 0.065846 .
Age 5.905e-02 1.061e+00 1.712e-02 3.448 0.000564 ***
Gendermale 9.005e-01 2.461e+00 3.147e-01 2.862 0.004213 **
Hypertension.compositeno -3.360e-02 9.670e-01 3.950e-01 -0.085 0.932211
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes 1.468e-01 1.158e+00 2.870e-01 0.511 0.609052
SmokerCurrentno -2.058e-01 8.140e-01 2.630e-01 -0.782 0.433941
SmokerCurrentyes NA NA 0.000e+00 NA NA
Med.Statin.LLDno 2.683e-01 1.308e+00 2.716e-01 0.988 0.323263
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 1.372e-01 1.147e+00 3.548e-01 0.387 0.698884
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD -1.148e-02 9.886e-01 6.503e-03 -1.766 0.077473 .
BMI 4.213e-02 1.043e+00 3.544e-02 1.189 0.234513
CAD_history 2.864e-02 1.029e+00 2.646e-01 0.108 0.913801
Stroke_history -1.326e-01 8.759e-01 2.609e-01 -0.508 0.611425
Peripheral.interv 6.794e-01 1.973e+00 2.736e-01 2.484 0.013009 *
stenose0-49% -1.532e+01 2.214e-07 4.315e+03 -0.004 0.997166
stenose50-70% -1.435e+00 2.380e-01 1.249e+00 -1.149 0.250518
stenose70-90% -4.917e-01 6.116e-01 1.032e+00 -0.476 0.633801
stenose90-99% -7.206e-01 4.865e-01 1.037e+00 -0.695 0.487213
stenose100% (Occlusion) 1.051e+00 2.862e+00 1.468e+00 0.716 0.473841
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% -1.500e+01 3.050e-07 4.201e+03 -0.004 0.997151
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
hsCRP_plasma 1.724e-03 1.002e+00 6.220e-04 2.772 0.005580 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00108,3.33109] 1.599e+00 6.255e-01 0.96973 2.636
Age 1.061e+00 9.427e-01 1.02581 1.097
Gendermale 2.461e+00 4.064e-01 1.32812 4.560
Hypertension.compositeno 9.670e-01 1.034e+00 0.44580 2.097
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 1.158e+00 8.635e-01 0.65990 2.032
SmokerCurrentno 8.140e-01 1.229e+00 0.48611 1.363
SmokerCurrentyes NA NA NA NA
Med.Statin.LLDno 1.308e+00 7.647e-01 0.76791 2.227
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 1.147e+00 8.718e-01 0.57228 2.299
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 9.886e-01 1.012e+00 0.97606 1.001
BMI 1.043e+00 9.587e-01 0.97304 1.118
CAD_history 1.029e+00 9.718e-01 0.61267 1.728
Stroke_history 8.759e-01 1.142e+00 0.52522 1.461
Peripheral.interv 1.973e+00 5.069e-01 1.15399 3.372
stenose0-49% 2.214e-07 4.518e+06 0.00000 Inf
stenose50-70% 2.380e-01 4.201e+00 0.02057 2.754
stenose70-90% 6.116e-01 1.635e+00 0.08087 4.625
stenose90-99% 4.865e-01 2.056e+00 0.06371 3.715
stenose100% (Occlusion) 2.862e+00 3.494e-01 0.16108 50.847
stenoseNA NA NA NA NA
stenose50-99% 3.050e-07 3.279e+06 0.00000 Inf
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
hsCRP_plasma 1.002e+00 9.983e-01 1.00050 1.003
Concordance= 0.703 (se = 0.033 )
Likelihood ratio test= 47.73 on 20 df, p=5e-04
Wald test = 40.43 on 20 df, p=0.004
Score (logrank) test = 45.02 on 20 df, p=0.001
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' IL6R_pg_ug_2015_rank ' and its association to ' epmajor.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epmajor.3years
Protein...................: IL6R_pg_ug_2015_rank
Effect size...............: 0.469189
Standard error............: 0.255067
Odds ratio (effect size)..: 1.599
Lower 95% CI..............: 0.97
Upper 95% CI..............: 2.636
T-value...................: 1.839474
P-value...................: 0.06584551
Sample size in model......: 623
Number of events..........: 73
> processing [MCP1_pg_ug_2015_rank]; 5 out of 5 proteins.
> cross tabulation of MCP1_pg_ug_2015_rank-stratum.
[-3.34148,0.00104) [ 0.00104,3.34148]
600 600
> fitting the model for MCP1_pg_ug_2015_rank-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + hsCRP_plasma, data = TEMP.DF)
n= 642, number of events= 74
(1746 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00104,3.34148] 2.201e-01 1.246e+00 2.431e-01 0.905 0.365212
Age 5.764e-02 1.059e+00 1.683e-02 3.426 0.000613 ***
Gendermale 8.810e-01 2.413e+00 3.078e-01 2.862 0.004206 **
Hypertension.compositeno -5.189e-02 9.494e-01 3.941e-01 -0.132 0.895245
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes 1.785e-01 1.195e+00 2.823e-01 0.632 0.527074
SmokerCurrentno -2.384e-01 7.879e-01 2.611e-01 -0.913 0.361350
SmokerCurrentyes NA NA 0.000e+00 NA NA
Med.Statin.LLDno 3.541e-01 1.425e+00 2.664e-01 1.329 0.183735
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 1.218e-01 1.130e+00 3.526e-01 0.345 0.729815
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD -1.364e-02 9.865e-01 6.516e-03 -2.093 0.036305 *
BMI 4.530e-02 1.046e+00 3.367e-02 1.345 0.178509
CAD_history -3.796e-02 9.628e-01 2.627e-01 -0.145 0.885093
Stroke_history -1.252e-01 8.823e-01 2.611e-01 -0.479 0.631705
Peripheral.interv 6.843e-01 1.982e+00 2.733e-01 2.504 0.012277 *
stenose0-49% -1.558e+01 1.705e-07 3.356e+03 -0.005 0.996295
stenose50-70% -1.149e+00 3.168e-01 1.240e+00 -0.927 0.354080
stenose70-90% -2.584e-01 7.723e-01 1.033e+00 -0.250 0.802554
stenose90-99% -3.903e-01 6.769e-01 1.037e+00 -0.376 0.706558
stenose100% (Occlusion) 1.598e+00 4.944e+00 1.484e+00 1.077 0.281380
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% -1.497e+01 3.145e-07 4.137e+03 -0.004 0.997112
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
hsCRP_plasma 1.664e-03 1.002e+00 6.027e-04 2.761 0.005764 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00104,3.34148] 1.246e+00 8.025e-01 0.77391 2.0067
Age 1.059e+00 9.440e-01 1.02497 1.0949
Gendermale 2.413e+00 4.144e-01 1.32013 4.4118
Hypertension.compositeno 9.494e-01 1.053e+00 0.43856 2.0554
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 1.195e+00 8.365e-01 0.68747 2.0789
SmokerCurrentno 7.879e-01 1.269e+00 0.47228 1.3145
SmokerCurrentyes NA NA NA NA
Med.Statin.LLDno 1.425e+00 7.018e-01 0.84537 2.4017
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 1.130e+00 8.853e-01 0.56592 2.2544
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 9.865e-01 1.014e+00 0.97393 0.9991
BMI 1.046e+00 9.557e-01 0.97952 1.1177
CAD_history 9.628e-01 1.039e+00 0.57535 1.6110
Stroke_history 8.823e-01 1.133e+00 0.52887 1.4721
Peripheral.interv 1.982e+00 5.045e-01 1.16030 3.3866
stenose0-49% 1.705e-07 5.866e+06 0.00000 Inf
stenose50-70% 3.168e-01 3.156e+00 0.02787 3.6020
stenose70-90% 7.723e-01 1.295e+00 0.10189 5.8538
stenose90-99% 6.769e-01 1.477e+00 0.08874 5.1628
stenose100% (Occlusion) 4.944e+00 2.023e-01 0.26991 90.5629
stenoseNA NA NA NA NA
stenose50-99% 3.145e-07 3.179e+06 0.00000 Inf
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
hsCRP_plasma 1.002e+00 9.983e-01 1.00048 1.0028
Concordance= 0.701 (se = 0.032 )
Likelihood ratio test= 46.5 on 20 df, p=7e-04
Wald test = 39.27 on 20 df, p=0.006
Score (logrank) test = 43.7 on 20 df, p=0.002
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_pg_ug_2015_rank ' and its association to ' epmajor.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epmajor.3years
Protein...................: MCP1_pg_ug_2015_rank
Effect size...............: 0.220083
Standard error............: 0.243057
Odds ratio (effect size)..: 1.246
Lower 95% CI..............: 0.774
Upper 95% CI..............: 2.007
T-value...................: 0.905479
P-value...................: 0.3652118
Sample size in model......: 642
Number of events..........: 74
* Analyzing the effect of plaque proteins on [epstroke.3years].
- creating temporary SE for this work.
- making a 'Surv' object and adding this to temporary dataframe.
- making strata of each of the plaque proteins and start survival analysis.
> processing [IL6_rank]; 1 out of 5 proteins.
> cross tabulation of IL6_rank-stratum.
[-1.48329,0.00474) [ 0.00474,3.10694]
265 264
> fitting the model for IL6_rank-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + hsCRP_plasma, data = TEMP.DF)
n= 268, number of events= 16
(2120 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00474,3.10694] -6.343e-01 5.303e-01 5.812e-01 -1.091 0.27513
Age 1.118e-01 1.118e+00 4.212e-02 2.655 0.00793 **
Gendermale 2.721e-01 1.313e+00 6.816e-01 0.399 0.68975
Hypertension.compositeno -3.726e-01 6.889e-01 8.437e-01 -0.442 0.65875
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes 2.760e-01 1.318e+00 7.372e-01 0.374 0.70815
SmokerCurrentno -3.276e-01 7.206e-01 5.847e-01 -0.560 0.57526
SmokerCurrentyes NA NA 0.000e+00 NA NA
Med.Statin.LLDno 6.236e-02 1.064e+00 6.046e-01 0.103 0.91784
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno -6.084e-01 5.442e-01 1.205e+00 -0.505 0.61357
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD 1.413e-02 1.014e+00 1.725e-02 0.819 0.41262
BMI 2.352e-02 1.024e+00 7.812e-02 0.301 0.76336
CAD_history 1.104e-01 1.117e+00 6.060e-01 0.182 0.85540
Stroke_history 8.518e-03 1.009e+00 5.725e-01 0.015 0.98813
Peripheral.interv -4.181e-01 6.583e-01 7.891e-01 -0.530 0.59618
stenose0-49% -1.949e+01 3.441e-09 1.647e+04 -0.001 0.99906
stenose50-70% -1.886e+01 6.418e-09 6.979e+03 -0.003 0.99784
stenose70-90% -2.966e+00 5.151e-02 1.357e+00 -2.186 0.02882 *
stenose90-99% -3.589e+00 2.763e-02 1.406e+00 -2.553 0.01068 *
stenose100% (Occlusion) NA NA 0.000e+00 NA NA
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% NA NA 0.000e+00 NA NA
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
hsCRP_plasma 4.996e-03 1.005e+00 1.756e-03 2.845 0.00444 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00474,3.10694] 5.303e-01 1.886e+00 0.169747 1.6568
Age 1.118e+00 8.942e-01 1.029711 1.2146
Gendermale 1.313e+00 7.618e-01 0.345159 4.9923
Hypertension.compositeno 6.889e-01 1.452e+00 0.131836 3.6002
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 1.318e+00 7.588e-01 0.310717 5.5889
SmokerCurrentno 7.206e-01 1.388e+00 0.229109 2.2667
SmokerCurrentyes NA NA NA NA
Med.Statin.LLDno 1.064e+00 9.395e-01 0.325439 3.4810
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 5.442e-01 1.837e+00 0.051321 5.7712
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 1.014e+00 9.860e-01 0.980521 1.0491
BMI 1.024e+00 9.768e-01 0.878449 1.1932
CAD_history 1.117e+00 8.955e-01 0.340531 3.6623
Stroke_history 1.009e+00 9.915e-01 0.328392 3.0975
Peripheral.interv 6.583e-01 1.519e+00 0.140194 3.0909
stenose0-49% 3.441e-09 2.907e+08 0.000000 Inf
stenose50-70% 6.418e-09 1.558e+08 0.000000 Inf
stenose70-90% 5.151e-02 1.941e+01 0.003605 0.7360
stenose90-99% 2.763e-02 3.620e+01 0.001757 0.4344
stenose100% (Occlusion) NA NA NA NA
stenoseNA NA NA NA NA
stenose50-99% NA NA NA NA
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
hsCRP_plasma 1.005e+00 9.950e-01 1.001556 1.0085
Concordance= 0.775 (se = 0.062 )
Likelihood ratio test= 24.03 on 18 df, p=0.2
Wald test = 13.98 on 18 df, p=0.7
Score (logrank) test = 69.62 on 18 df, p=5e-08
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' IL6_rank ' and its association to ' epstroke.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epstroke.3years
Protein...................: IL6_rank
Effect size...............: -0.634292
Standard error............: 0.581212
Odds ratio (effect size)..: 0.53
Lower 95% CI..............: 0.17
Upper 95% CI..............: 1.657
T-value...................: -1.091327
P-value...................: 0.2751288
Sample size in model......: 268
Number of events..........: 16
> processing [MCP1_rank]; 2 out of 5 proteins.
> cross tabulation of MCP1_rank-stratum.
[-2.41053,0.00444) [ 0.00444,3.12635]
283 282
> fitting the model for MCP1_rank-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + hsCRP_plasma, data = TEMP.DF)
n= 282, number of events= 17
(2106 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00444,3.12635] -1.353e+00 2.584e-01 5.944e-01 -2.276 0.02282 *
Age 7.559e-02 1.079e+00 3.884e-02 1.946 0.05162 .
Gendermale 6.031e-01 1.828e+00 6.803e-01 0.886 0.37539
Hypertension.compositeno 8.003e-03 1.008e+00 8.095e-01 0.010 0.99211
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes -2.550e-01 7.749e-01 7.332e-01 -0.348 0.72802
SmokerCurrentno -1.702e-01 8.435e-01 5.799e-01 -0.293 0.76919
SmokerCurrentyes NA NA 0.000e+00 NA NA
Med.Statin.LLDno 2.128e-02 1.022e+00 5.846e-01 0.036 0.97097
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno -1.196e+00 3.025e-01 1.332e+00 -0.898 0.36918
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD 1.406e-02 1.014e+00 1.608e-02 0.874 0.38201
BMI 7.504e-02 1.078e+00 6.347e-02 1.182 0.23709
CAD_history -5.998e-02 9.418e-01 5.898e-01 -0.102 0.91899
Stroke_history -2.604e-01 7.707e-01 5.579e-01 -0.467 0.64066
Peripheral.interv -5.122e-01 5.992e-01 7.849e-01 -0.652 0.51409
stenose0-49% -1.930e+01 4.153e-09 1.660e+04 -0.001 0.99907
stenose50-70% -1.950e+01 3.405e-09 6.144e+03 -0.003 0.99747
stenose70-90% -3.365e+00 3.456e-02 1.443e+00 -2.333 0.01967 *
stenose90-99% -3.694e+00 2.487e-02 1.447e+00 -2.552 0.01070 *
stenose100% (Occlusion) NA NA 0.000e+00 NA NA
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% NA NA 0.000e+00 NA NA
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
hsCRP_plasma 5.712e-03 1.006e+00 1.759e-03 3.248 0.00116 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00444,3.12635] 2.584e-01 3.870e+00 0.080610 0.8285
Age 1.079e+00 9.272e-01 0.999468 1.1638
Gendermale 1.828e+00 5.471e-01 0.481725 6.9347
Hypertension.compositeno 1.008e+00 9.920e-01 0.206272 4.9262
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 7.749e-01 1.290e+00 0.184132 3.2613
SmokerCurrentno 8.435e-01 1.186e+00 0.270693 2.6286
SmokerCurrentyes NA NA NA NA
Med.Statin.LLDno 1.022e+00 9.789e-01 0.324801 3.2126
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 3.025e-01 3.306e+00 0.022246 4.1127
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 1.014e+00 9.860e-01 0.982693 1.0466
BMI 1.078e+00 9.277e-01 0.951838 1.2207
CAD_history 9.418e-01 1.062e+00 0.296414 2.9923
Stroke_history 7.707e-01 1.297e+00 0.258260 2.3002
Peripheral.interv 5.992e-01 1.669e+00 0.128657 2.7907
stenose0-49% 4.153e-09 2.408e+08 0.000000 Inf
stenose50-70% 3.405e-09 2.937e+08 0.000000 Inf
stenose70-90% 3.456e-02 2.894e+01 0.002045 0.5841
stenose90-99% 2.487e-02 4.022e+01 0.001457 0.4243
stenose100% (Occlusion) NA NA NA NA
stenoseNA NA NA NA NA
stenose50-99% NA NA NA NA
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
hsCRP_plasma 1.006e+00 9.943e-01 1.002267 1.0092
Concordance= 0.778 (se = 0.044 )
Likelihood ratio test= 27.05 on 18 df, p=0.08
Wald test = 17.77 on 18 df, p=0.5
Score (logrank) test = 74.01 on 18 df, p=9e-09
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_rank ' and its association to ' epstroke.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epstroke.3years
Protein...................: MCP1_rank
Effect size...............: -1.353138
Standard error............: 0.594398
Odds ratio (effect size)..: 0.258
Lower 95% CI..............: 0.081
Upper 95% CI..............: 0.829
T-value...................: -2.276484
P-value...................: 0.02281707
Sample size in model......: 282
Number of events..........: 17
> processing [IL6_pg_ug_2015_rank]; 3 out of 5 proteins.
> cross tabulation of IL6_pg_ug_2015_rank-stratum.
[-3.33061,0.00109) [ 0.00109,3.33061]
577 577
> fitting the model for IL6_pg_ug_2015_rank-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + hsCRP_plasma, data = TEMP.DF)
n= 618, number of events= 36
(1770 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00109,3.33061] -7.706e-02 9.258e-01 3.574e-01 -0.216 0.829293
Age 1.048e-01 1.110e+00 2.521e-02 4.155 3.25e-05 ***
Gendermale 1.112e+00 3.040e+00 4.667e-01 2.382 0.017222 *
Hypertension.compositeno 2.472e-01 1.280e+00 4.770e-01 0.518 0.604307
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes 1.545e-01 1.167e+00 4.216e-01 0.366 0.714022
SmokerCurrentno 5.431e-02 1.056e+00 4.023e-01 0.135 0.892602
SmokerCurrentyes NA NA 0.000e+00 NA NA
Med.Statin.LLDno 5.182e-01 1.679e+00 3.595e-01 1.442 0.149407
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 8.201e-02 1.085e+00 5.079e-01 0.161 0.871733
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD 6.588e-03 1.007e+00 9.032e-03 0.729 0.465734
BMI 1.056e-01 1.111e+00 4.614e-02 2.288 0.022120 *
CAD_history -9.445e-01 3.889e-01 4.605e-01 -2.051 0.040259 *
Stroke_history 1.343e-01 1.144e+00 3.670e-01 0.366 0.714459
Peripheral.interv 9.068e-01 2.476e+00 4.014e-01 2.259 0.023885 *
stenose0-49% -2.542e-01 7.755e-01 7.243e+03 0.000 0.999972
stenose50-70% 1.562e+01 6.097e+06 4.571e+03 0.003 0.997273
stenose70-90% 1.597e+01 8.649e+06 4.571e+03 0.003 0.997212
stenose90-99% 1.576e+01 6.960e+06 4.571e+03 0.003 0.997250
stenose100% (Occlusion) 1.859e+01 1.188e+08 4.571e+03 0.004 0.996755
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% NA NA 0.000e+00 NA NA
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
hsCRP_plasma 2.753e-03 1.003e+00 7.714e-04 3.569 0.000359 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00109,3.33061] 9.258e-01 1.080e+00 0.4595 1.8653
Age 1.110e+00 9.005e-01 1.0569 1.1667
Gendermale 3.040e+00 3.290e-01 1.2177 7.5878
Hypertension.compositeno 1.280e+00 7.810e-01 0.5027 3.2615
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 1.167e+00 8.569e-01 0.5108 2.6665
SmokerCurrentno 1.056e+00 9.471e-01 0.4799 2.3227
SmokerCurrentyes NA NA NA NA
Med.Statin.LLDno 1.679e+00 5.956e-01 0.8300 3.3966
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 1.085e+00 9.213e-01 0.4011 2.9374
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 1.007e+00 9.934e-01 0.9889 1.0246
BMI 1.111e+00 8.998e-01 1.0153 1.2165
CAD_history 3.889e-01 2.572e+00 0.1577 0.9589
Stroke_history 1.144e+00 8.744e-01 0.5571 2.3480
Peripheral.interv 2.476e+00 4.038e-01 1.1275 5.4385
stenose0-49% 7.755e-01 1.289e+00 0.0000 Inf
stenose50-70% 6.097e+06 1.640e-07 0.0000 Inf
stenose70-90% 8.649e+06 1.156e-07 0.0000 Inf
stenose90-99% 6.960e+06 1.437e-07 0.0000 Inf
stenose100% (Occlusion) 1.188e+08 8.416e-09 0.0000 Inf
stenoseNA NA NA NA NA
stenose50-99% NA NA NA NA
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
hsCRP_plasma 1.003e+00 9.973e-01 1.0012 1.0043
Concordance= 0.751 (se = 0.045 )
Likelihood ratio test= 41.44 on 19 df, p=0.002
Wald test = 34.59 on 19 df, p=0.02
Score (logrank) test = 40.43 on 19 df, p=0.003
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' IL6_pg_ug_2015_rank ' and its association to ' epstroke.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epstroke.3years
Protein...................: IL6_pg_ug_2015_rank
Effect size...............: -0.077056
Standard error............: 0.357389
Odds ratio (effect size)..: 0.926
Lower 95% CI..............: 0.46
Upper 95% CI..............: 1.865
T-value...................: -0.215609
P-value...................: 0.8292928
Sample size in model......: 618
Number of events..........: 36
> processing [IL6R_pg_ug_2015_rank]; 4 out of 5 proteins.
> cross tabulation of IL6R_pg_ug_2015_rank-stratum.
[-3.33109,0.00108) [ 0.00108,3.33109]
578 578
> fitting the model for IL6R_pg_ug_2015_rank-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + hsCRP_plasma, data = TEMP.DF)
n= 623, number of events= 38
(1765 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00108,3.33109] 1.328e-01 1.142e+00 3.479e-01 0.382 0.702622
Age 9.242e-02 1.097e+00 2.385e-02 3.875 0.000106 ***
Gendermale 9.010e-01 2.462e+00 4.339e-01 2.076 0.037867 *
Hypertension.compositeno 3.920e-01 1.480e+00 4.465e-01 0.878 0.380042
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes 8.893e-02 1.093e+00 4.131e-01 0.215 0.829560
SmokerCurrentno 2.213e-01 1.248e+00 3.964e-01 0.558 0.576680
SmokerCurrentyes NA NA 0.000e+00 NA NA
Med.Statin.LLDno 5.507e-01 1.734e+00 3.524e-01 1.563 0.118089
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 6.852e-03 1.007e+00 5.053e-01 0.014 0.989181
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD 8.250e-03 1.008e+00 8.828e-03 0.935 0.350035
BMI 1.240e-01 1.132e+00 4.815e-02 2.576 0.009999 **
CAD_history -7.710e-01 4.625e-01 4.351e-01 -1.772 0.076349 .
Stroke_history 1.645e-01 1.179e+00 3.523e-01 0.467 0.640471
Peripheral.interv 8.030e-01 2.232e+00 3.973e-01 2.021 0.043271 *
stenose0-49% 7.960e-01 2.217e+00 1.113e+04 0.000 0.999943
stenose50-70% 1.592e+01 8.239e+06 5.452e+03 0.003 0.997669
stenose70-90% 1.640e+01 1.321e+07 5.452e+03 0.003 0.997600
stenose90-99% 1.608e+01 9.642e+06 5.452e+03 0.003 0.997646
stenose100% (Occlusion) 1.888e+01 1.579e+08 5.452e+03 0.003 0.997237
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% 6.571e-01 1.929e+00 1.157e+04 0.000 0.999955
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
hsCRP_plasma 2.565e-03 1.003e+00 7.306e-04 3.510 0.000448 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00108,3.33109] 1.142e+00 8.756e-01 0.5775 2.259
Age 1.097e+00 9.117e-01 1.0467 1.149
Gendermale 2.462e+00 4.062e-01 1.0518 5.763
Hypertension.compositeno 1.480e+00 6.757e-01 0.6168 3.551
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 1.093e+00 9.149e-01 0.4864 2.456
SmokerCurrentno 1.248e+00 8.015e-01 0.5737 2.714
SmokerCurrentyes NA NA NA NA
Med.Statin.LLDno 1.734e+00 5.766e-01 0.8694 3.460
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 1.007e+00 9.932e-01 0.3740 2.711
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 1.008e+00 9.918e-01 0.9910 1.026
BMI 1.132e+00 8.834e-01 1.0301 1.244
CAD_history 4.625e-01 2.162e+00 0.1972 1.085
Stroke_history 1.179e+00 8.483e-01 0.5910 2.351
Peripheral.interv 2.232e+00 4.480e-01 1.0246 4.863
stenose0-49% 2.217e+00 4.511e-01 0.0000 Inf
stenose50-70% 8.239e+06 1.214e-07 0.0000 Inf
stenose70-90% 1.321e+07 7.571e-08 0.0000 Inf
stenose90-99% 9.642e+06 1.037e-07 0.0000 Inf
stenose100% (Occlusion) 1.579e+08 6.333e-09 0.0000 Inf
stenoseNA NA NA NA NA
stenose50-99% 1.929e+00 5.183e-01 0.0000 Inf
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
hsCRP_plasma 1.003e+00 9.974e-01 1.0011 1.004
Concordance= 0.74 (se = 0.045 )
Likelihood ratio test= 39.66 on 20 df, p=0.006
Wald test = 33.6 on 20 df, p=0.03
Score (logrank) test = 39.52 on 20 df, p=0.006
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' IL6R_pg_ug_2015_rank ' and its association to ' epstroke.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epstroke.3years
Protein...................: IL6R_pg_ug_2015_rank
Effect size...............: 0.132838
Standard error............: 0.347941
Odds ratio (effect size)..: 1.142
Lower 95% CI..............: 0.577
Upper 95% CI..............: 2.259
T-value...................: 0.381784
P-value...................: 0.7026217
Sample size in model......: 623
Number of events..........: 38
> processing [MCP1_pg_ug_2015_rank]; 5 out of 5 proteins.
> cross tabulation of MCP1_pg_ug_2015_rank-stratum.
[-3.34148,0.00104) [ 0.00104,3.34148]
600 600
> fitting the model for MCP1_pg_ug_2015_rank-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + hsCRP_plasma, data = TEMP.DF)
n= 642, number of events= 39
(1746 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00104,3.34148] 1.603e-01 1.174e+00 3.445e-01 0.465 0.641655
Age 9.215e-02 1.097e+00 2.345e-02 3.929 8.53e-05 ***
Gendermale 8.195e-01 2.269e+00 4.185e-01 1.958 0.050198 .
Hypertension.compositeno 3.903e-01 1.477e+00 4.448e-01 0.877 0.380225
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes 1.920e-01 1.212e+00 4.029e-01 0.477 0.633640
SmokerCurrentno 2.378e-01 1.268e+00 3.930e-01 0.605 0.545145
SmokerCurrentyes NA NA 0.000e+00 NA NA
Med.Statin.LLDno 5.567e-01 1.745e+00 3.438e-01 1.619 0.105420
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno -3.158e-02 9.689e-01 5.040e-01 -0.063 0.950038
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD 5.474e-03 1.005e+00 8.808e-03 0.621 0.534299
BMI 1.111e-01 1.118e+00 4.235e-02 2.624 0.008682 **
CAD_history -8.088e-01 4.454e-01 4.280e-01 -1.890 0.058786 .
Stroke_history 1.279e-01 1.136e+00 3.524e-01 0.363 0.716665
Peripheral.interv 7.314e-01 2.078e+00 3.961e-01 1.846 0.064855 .
stenose0-49% -1.147e-02 9.886e-01 7.076e+03 0.000 0.999999
stenose50-70% 1.553e+01 5.543e+06 4.362e+03 0.004 0.997160
stenose70-90% 1.601e+01 8.936e+06 4.362e+03 0.004 0.997072
stenose90-99% 1.581e+01 7.344e+06 4.362e+03 0.004 0.997108
stenose100% (Occlusion) 1.865e+01 1.253e+08 4.362e+03 0.004 0.996589
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% 8.436e-01 2.325e+00 8.528e+03 0.000 0.999921
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
hsCRP_plasma 2.534e-03 1.003e+00 7.256e-04 3.492 0.000479 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00104,3.34148] 1.174e+00 8.519e-01 0.5976 2.306
Age 1.097e+00 9.120e-01 1.0473 1.148
Gendermale 2.269e+00 4.407e-01 0.9993 5.154
Hypertension.compositeno 1.477e+00 6.768e-01 0.6178 3.533
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 1.212e+00 8.253e-01 0.5501 2.669
SmokerCurrentno 1.268e+00 7.884e-01 0.5871 2.740
SmokerCurrentyes NA NA NA NA
Med.Statin.LLDno 1.745e+00 5.731e-01 0.8894 3.423
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 9.689e-01 1.032e+00 0.3608 2.602
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 1.005e+00 9.945e-01 0.9883 1.023
BMI 1.118e+00 8.948e-01 1.0285 1.214
CAD_history 4.454e-01 2.245e+00 0.1925 1.030
Stroke_history 1.136e+00 8.799e-01 0.5696 2.267
Peripheral.interv 2.078e+00 4.812e-01 0.9560 4.517
stenose0-49% 9.886e-01 1.012e+00 0.0000 Inf
stenose50-70% 5.543e+06 1.804e-07 0.0000 Inf
stenose70-90% 8.936e+06 1.119e-07 0.0000 Inf
stenose90-99% 7.344e+06 1.362e-07 0.0000 Inf
stenose100% (Occlusion) 1.253e+08 7.980e-09 0.0000 Inf
stenoseNA NA NA NA NA
stenose50-99% 2.325e+00 4.302e-01 0.0000 Inf
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
hsCRP_plasma 1.003e+00 9.975e-01 1.0011 1.004
Concordance= 0.743 (se = 0.043 )
Likelihood ratio test= 39.62 on 20 df, p=0.006
Wald test = 33.61 on 20 df, p=0.03
Score (logrank) test = 39.6 on 20 df, p=0.006
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_pg_ug_2015_rank ' and its association to ' epstroke.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epstroke.3years
Protein...................: MCP1_pg_ug_2015_rank
Effect size...............: 0.160313
Standard error............: 0.344474
Odds ratio (effect size)..: 1.174
Lower 95% CI..............: 0.598
Upper 95% CI..............: 2.306
T-value...................: 0.465386
P-value...................: 0.6416551
Sample size in model......: 642
Number of events..........: 39
* Analyzing the effect of plaque proteins on [epcoronary.3years].
- creating temporary SE for this work.
- making a 'Surv' object and adding this to temporary dataframe.
- making strata of each of the plaque proteins and start survival analysis.
> processing [IL6_rank]; 1 out of 5 proteins.
> cross tabulation of IL6_rank-stratum.
[-1.48329,0.00474) [ 0.00474,3.10694]
265 264
> fitting the model for IL6_rank-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + hsCRP_plasma, data = TEMP.DF)
n= 268, number of events= 26
(2120 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00474,3.10694] -1.951e-01 8.228e-01 4.190e-01 -0.466 0.64150
Age 8.561e-02 1.089e+00 3.252e-02 2.633 0.00847 **
Gendermale 5.439e-01 1.723e+00 5.294e-01 1.027 0.30427
Hypertension.compositeno -5.772e-01 5.614e-01 7.702e-01 -0.750 0.45355
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes 6.536e-01 1.922e+00 4.900e-01 1.334 0.18221
SmokerCurrentno -4.073e-01 6.655e-01 4.363e-01 -0.934 0.35055
SmokerCurrentyes NA NA 0.000e+00 NA NA
Med.Statin.LLDno -7.107e-01 4.913e-01 5.438e-01 -1.307 0.19126
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 8.448e-02 1.088e+00 7.544e-01 0.112 0.91085
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD 4.736e-03 1.005e+00 1.300e-02 0.364 0.71566
BMI -2.461e-03 9.975e-01 5.606e-02 -0.044 0.96498
CAD_history 6.008e-01 1.824e+00 4.462e-01 1.346 0.17816
Stroke_history -4.277e-01 6.520e-01 4.858e-01 -0.880 0.37866
Peripheral.interv 2.224e-01 1.249e+00 4.758e-01 0.467 0.64021
stenose0-49% -1.167e+00 3.114e-01 2.163e+04 0.000 0.99996
stenose50-70% -7.764e-02 9.253e-01 1.211e+04 0.000 0.99999
stenose70-90% 1.676e+01 1.898e+07 9.270e+03 0.002 0.99856
stenose90-99% 1.607e+01 9.502e+06 9.270e+03 0.002 0.99862
stenose100% (Occlusion) NA NA 0.000e+00 NA NA
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% NA NA 0.000e+00 NA NA
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
hsCRP_plasma 1.489e-03 1.001e+00 1.290e-03 1.154 0.24864
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00474,3.10694] 8.228e-01 1.215e+00 0.3619 1.870
Age 1.089e+00 9.180e-01 1.0221 1.161
Gendermale 1.723e+00 5.805e-01 0.6103 4.862
Hypertension.compositeno 5.614e-01 1.781e+00 0.1241 2.540
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 1.922e+00 5.202e-01 0.7359 5.022
SmokerCurrentno 6.655e-01 1.503e+00 0.2830 1.565
SmokerCurrentyes NA NA NA NA
Med.Statin.LLDno 4.913e-01 2.035e+00 0.1692 1.426
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 1.088e+00 9.190e-01 0.2480 4.774
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 1.005e+00 9.953e-01 0.9795 1.031
BMI 9.975e-01 1.002e+00 0.8937 1.113
CAD_history 1.824e+00 5.484e-01 0.7605 4.373
Stroke_history 6.520e-01 1.534e+00 0.2516 1.690
Peripheral.interv 1.249e+00 8.006e-01 0.4916 3.174
stenose0-49% 3.114e-01 3.211e+00 0.0000 Inf
stenose50-70% 9.253e-01 1.081e+00 0.0000 Inf
stenose70-90% 1.898e+07 5.269e-08 0.0000 Inf
stenose90-99% 9.502e+06 1.052e-07 0.0000 Inf
stenose100% (Occlusion) NA NA NA NA
stenoseNA NA NA NA NA
stenose50-99% NA NA NA NA
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
hsCRP_plasma 1.001e+00 9.985e-01 0.9990 1.004
Concordance= 0.748 (se = 0.04 )
Likelihood ratio test= 22.38 on 18 df, p=0.2
Wald test = 16.91 on 18 df, p=0.5
Score (logrank) test = 20.49 on 18 df, p=0.3
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' IL6_rank ' and its association to ' epcoronary.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epcoronary.3years
Protein...................: IL6_rank
Effect size...............: -0.195076
Standard error............: 0.418981
Odds ratio (effect size)..: 0.823
Lower 95% CI..............: 0.362
Upper 95% CI..............: 1.87
T-value...................: -0.465596
P-value...................: 0.6415048
Sample size in model......: 268
Number of events..........: 26
> processing [MCP1_rank]; 2 out of 5 proteins.
> cross tabulation of MCP1_rank-stratum.
[-2.41053,0.00444) [ 0.00444,3.12635]
283 282
> fitting the model for MCP1_rank-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + hsCRP_plasma, data = TEMP.DF)
n= 282, number of events= 26
(2106 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00444,3.12635] 2.502e-01 1.284e+00 4.203e-01 0.595 0.55159
Age 8.205e-02 1.086e+00 3.145e-02 2.609 0.00909 **
Gendermale 4.205e-01 1.523e+00 5.317e-01 0.791 0.42897
Hypertension.compositeno -5.981e-01 5.498e-01 7.660e-01 -0.781 0.43490
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes 8.161e-01 2.262e+00 4.796e-01 1.701 0.08887 .
SmokerCurrentno -6.407e-01 5.269e-01 4.264e-01 -1.503 0.13292
SmokerCurrentyes NA NA 0.000e+00 NA NA
Med.Statin.LLDno -5.710e-01 5.650e-01 5.466e-01 -1.045 0.29618
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno -2.510e-01 7.780e-01 7.633e-01 -0.329 0.74230
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD 5.054e-03 1.005e+00 1.260e-02 0.401 0.68826
BMI 3.259e-03 1.003e+00 5.345e-02 0.061 0.95137
CAD_history 5.956e-01 1.814e+00 4.438e-01 1.342 0.17962
Stroke_history -3.392e-01 7.124e-01 4.700e-01 -0.722 0.47055
Peripheral.interv 5.946e-01 1.812e+00 4.567e-01 1.302 0.19292
stenose0-49% -1.809e+00 1.638e-01 1.440e+04 0.000 0.99990
stenose50-70% -3.731e-01 6.886e-01 8.607e+03 0.000 0.99997
stenose70-90% 1.555e+01 5.656e+06 7.113e+03 0.002 0.99826
stenose90-99% 1.486e+01 2.855e+06 7.113e+03 0.002 0.99833
stenose100% (Occlusion) NA NA 0.000e+00 NA NA
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% NA NA 0.000e+00 NA NA
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
hsCRP_plasma 1.423e-03 1.001e+00 1.312e-03 1.084 0.27828
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00444,3.12635] 1.284e+00 7.786e-01 0.5635 2.927
Age 1.086e+00 9.212e-01 1.0206 1.155
Gendermale 1.523e+00 6.567e-01 0.5371 4.317
Hypertension.compositeno 5.498e-01 1.819e+00 0.1225 2.468
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 2.262e+00 4.422e-01 0.8834 5.790
SmokerCurrentno 5.269e-01 1.898e+00 0.2285 1.215
SmokerCurrentyes NA NA NA NA
Med.Statin.LLDno 5.650e-01 1.770e+00 0.1935 1.649
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 7.780e-01 1.285e+00 0.1743 3.473
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 1.005e+00 9.950e-01 0.9806 1.030
BMI 1.003e+00 9.967e-01 0.9035 1.114
CAD_history 1.814e+00 5.512e-01 0.7601 4.330
Stroke_history 7.124e-01 1.404e+00 0.2835 1.790
Peripheral.interv 1.812e+00 5.518e-01 0.7405 4.436
stenose0-49% 1.638e-01 6.104e+00 0.0000 Inf
stenose50-70% 6.886e-01 1.452e+00 0.0000 Inf
stenose70-90% 5.656e+06 1.768e-07 0.0000 Inf
stenose90-99% 2.855e+06 3.502e-07 0.0000 Inf
stenose100% (Occlusion) NA NA NA NA
stenoseNA NA NA NA NA
stenose50-99% NA NA NA NA
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
hsCRP_plasma 1.001e+00 9.986e-01 0.9989 1.004
Concordance= 0.743 (se = 0.042 )
Likelihood ratio test= 22.86 on 18 df, p=0.2
Wald test = 18.54 on 18 df, p=0.4
Score (logrank) test = 22.18 on 18 df, p=0.2
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_rank ' and its association to ' epcoronary.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epcoronary.3years
Protein...................: MCP1_rank
Effect size...............: 0.250218
Standard error............: 0.420272
Odds ratio (effect size)..: 1.284
Lower 95% CI..............: 0.564
Upper 95% CI..............: 2.927
T-value...................: 0.595372
P-value...................: 0.5515948
Sample size in model......: 282
Number of events..........: 26
> processing [IL6_pg_ug_2015_rank]; 3 out of 5 proteins.
> cross tabulation of IL6_pg_ug_2015_rank-stratum.
[-3.33061,0.00109) [ 0.00109,3.33061]
577 577
> fitting the model for IL6_pg_ug_2015_rank-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + hsCRP_plasma, data = TEMP.DF)
n= 618, number of events= 47
(1770 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00109,3.33061] 3.396e-01 1.404e+00 3.052e-01 1.113 0.265852
Age -5.270e-03 9.947e-01 2.040e-02 -0.258 0.796187
Gendermale 9.025e-01 2.466e+00 4.015e-01 2.248 0.024587 *
Hypertension.compositeno -9.062e-01 4.040e-01 7.423e-01 -1.221 0.222132
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes 1.538e-01 1.166e+00 3.464e-01 0.444 0.657127
SmokerCurrentno -7.453e-01 4.746e-01 3.139e-01 -2.374 0.017580 *
SmokerCurrentyes NA NA 0.000e+00 NA NA
Med.Statin.LLDno -8.568e-02 9.179e-01 3.868e-01 -0.222 0.824695
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 3.711e-02 1.038e+00 4.917e-01 0.075 0.939834
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD -1.146e-02 9.886e-01 8.202e-03 -1.397 0.162283
BMI -1.389e-02 9.862e-01 4.262e-02 -0.326 0.744519
CAD_history 1.124e+00 3.076e+00 3.222e-01 3.488 0.000487 ***
Stroke_history 3.471e-02 1.035e+00 3.271e-01 0.106 0.915502
Peripheral.interv 4.500e-01 1.568e+00 3.286e-01 1.369 0.170844
stenose0-49% -1.835e+01 1.077e-08 1.248e+04 -0.001 0.998827
stenose50-70% -1.831e+01 1.113e-08 3.039e+03 -0.006 0.995192
stenose70-90% -1.290e+00 2.754e-01 1.053e+00 -1.225 0.220712
stenose90-99% -1.479e+00 2.278e-01 1.054e+00 -1.403 0.160577
stenose100% (Occlusion) -1.868e+01 7.697e-09 9.605e+03 -0.002 0.998448
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% NA NA 0.000e+00 NA NA
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
hsCRP_plasma 7.612e-05 1.000e+00 9.969e-04 0.076 0.939134
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00109,3.33061] 1.404e+00 7.121e-01 0.77214 2.554
Age 9.947e-01 1.005e+00 0.95575 1.035
Gendermale 2.466e+00 4.056e-01 1.12252 5.416
Hypertension.compositeno 4.040e-01 2.475e+00 0.09432 1.731
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 1.166e+00 8.575e-01 0.59148 2.299
SmokerCurrentno 4.746e-01 2.107e+00 0.25651 0.878
SmokerCurrentyes NA NA NA NA
Med.Statin.LLDno 9.179e-01 1.089e+00 0.43006 1.959
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 1.038e+00 9.636e-01 0.39592 2.720
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 9.886e-01 1.012e+00 0.97284 1.005
BMI 9.862e-01 1.014e+00 0.90716 1.072
CAD_history 3.076e+00 3.251e-01 1.63600 5.785
Stroke_history 1.035e+00 9.659e-01 0.54531 1.966
Peripheral.interv 1.568e+00 6.376e-01 0.82363 2.986
stenose0-49% 1.077e-08 9.283e+07 0.00000 Inf
stenose50-70% 1.113e-08 8.988e+07 0.00000 Inf
stenose70-90% 2.754e-01 3.631e+00 0.03496 2.169
stenose90-99% 2.278e-01 4.389e+00 0.02886 1.799
stenose100% (Occlusion) 7.697e-09 1.299e+08 0.00000 Inf
stenoseNA NA NA NA NA
stenose50-99% NA NA NA NA
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
hsCRP_plasma 1.000e+00 9.999e-01 0.99812 1.002
Concordance= 0.763 (se = 0.034 )
Likelihood ratio test= 43.68 on 19 df, p=0.001
Wald test = 30.52 on 19 df, p=0.05
Score (logrank) test = 42.26 on 19 df, p=0.002
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' IL6_pg_ug_2015_rank ' and its association to ' epcoronary.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epcoronary.3years
Protein...................: IL6_pg_ug_2015_rank
Effect size...............: 0.339572
Standard error............: 0.305188
Odds ratio (effect size)..: 1.404
Lower 95% CI..............: 0.772
Upper 95% CI..............: 2.554
T-value...................: 1.112665
P-value...................: 0.2658522
Sample size in model......: 618
Number of events..........: 47
> processing [IL6R_pg_ug_2015_rank]; 4 out of 5 proteins.
> cross tabulation of IL6R_pg_ug_2015_rank-stratum.
[-3.33109,0.00108) [ 0.00108,3.33109]
578 578
> fitting the model for IL6R_pg_ug_2015_rank-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + hsCRP_plasma, data = TEMP.DF)
n= 623, number of events= 48
(1765 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00108,3.33109] 6.164e-01 1.852e+00 3.156e-01 1.953 0.05081 .
Age -2.033e-03 9.980e-01 2.005e-02 -0.101 0.91925
Gendermale 6.907e-01 1.995e+00 3.815e-01 1.811 0.07018 .
Hypertension.compositeno -9.056e-01 4.043e-01 7.411e-01 -1.222 0.22172
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes 1.721e-01 1.188e+00 3.477e-01 0.495 0.62060
SmokerCurrentno -5.742e-01 5.632e-01 3.109e-01 -1.847 0.06479 .
SmokerCurrentyes NA NA 0.000e+00 NA NA
Med.Statin.LLDno -1.747e-01 8.397e-01 3.898e-01 -0.448 0.65406
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 1.668e-02 1.017e+00 4.932e-01 0.034 0.97302
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD -1.222e-02 9.879e-01 8.077e-03 -1.513 0.13033
BMI -2.167e-02 9.786e-01 4.284e-02 -0.506 0.61295
CAD_history 1.029e+00 2.799e+00 3.163e-01 3.253 0.00114 **
Stroke_history -1.088e-02 9.892e-01 3.259e-01 -0.033 0.97337
Peripheral.interv 3.127e-01 1.367e+00 3.270e-01 0.956 0.33893
stenose0-49% -1.842e+01 1.005e-08 1.484e+04 -0.001 0.99901
stenose50-70% -1.838e+01 1.044e-08 2.972e+03 -0.006 0.99507
stenose70-90% -1.422e+00 2.413e-01 1.061e+00 -1.340 0.18020
stenose90-99% -1.576e+00 2.067e-01 1.063e+00 -1.483 0.13801
stenose100% (Occlusion) -1.873e+01 7.313e-09 8.875e+03 -0.002 0.99832
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% -1.827e+01 1.165e-08 1.496e+04 -0.001 0.99903
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
hsCRP_plasma 2.533e-04 1.000e+00 1.013e-03 0.250 0.80256
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00108,3.33109] 1.852e+00 5.399e-01 0.99784 3.438
Age 9.980e-01 1.002e+00 0.95951 1.038
Gendermale 1.995e+00 5.012e-01 0.94468 4.214
Hypertension.compositeno 4.043e-01 2.473e+00 0.09461 1.728
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 1.188e+00 8.419e-01 0.60085 2.348
SmokerCurrentno 5.632e-01 1.776e+00 0.30619 1.036
SmokerCurrentyes NA NA NA NA
Med.Statin.LLDno 8.397e-01 1.191e+00 0.39114 1.803
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 1.017e+00 9.835e-01 0.38674 2.673
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 9.879e-01 1.012e+00 0.97234 1.004
BMI 9.786e-01 1.022e+00 0.89976 1.064
CAD_history 2.799e+00 3.573e-01 1.50554 5.202
Stroke_history 9.892e-01 1.011e+00 0.52226 1.874
Peripheral.interv 1.367e+00 7.315e-01 0.72024 2.595
stenose0-49% 1.005e-08 9.949e+07 0.00000 Inf
stenose50-70% 1.044e-08 9.582e+07 0.00000 Inf
stenose70-90% 2.413e-01 4.145e+00 0.03016 1.930
stenose90-99% 2.067e-01 4.837e+00 0.02575 1.660
stenose100% (Occlusion) 7.313e-09 1.367e+08 0.00000 Inf
stenoseNA NA NA NA NA
stenose50-99% 1.165e-08 8.583e+07 0.00000 Inf
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
hsCRP_plasma 1.000e+00 9.997e-01 0.99827 1.002
Concordance= 0.754 (se = 0.037 )
Likelihood ratio test= 41.86 on 20 df, p=0.003
Wald test = 29.23 on 20 df, p=0.08
Score (logrank) test = 40.5 on 20 df, p=0.004
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' IL6R_pg_ug_2015_rank ' and its association to ' epcoronary.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epcoronary.3years
Protein...................: IL6R_pg_ug_2015_rank
Effect size...............: 0.616391
Standard error............: 0.315596
Odds ratio (effect size)..: 1.852
Lower 95% CI..............: 0.998
Upper 95% CI..............: 3.438
T-value...................: 1.953101
P-value...................: 0.05080762
Sample size in model......: 623
Number of events..........: 48
> processing [MCP1_pg_ug_2015_rank]; 5 out of 5 proteins.
> cross tabulation of MCP1_pg_ug_2015_rank-stratum.
[-3.34148,0.00104) [ 0.00104,3.34148]
600 600
> fitting the model for MCP1_pg_ug_2015_rank-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + hsCRP_plasma, data = TEMP.DF)
n= 642, number of events= 48
(1746 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00104,3.34148] 1.453e-01 1.156e+00 2.985e-01 0.487 0.62654
Age -6.351e-03 9.937e-01 1.984e-02 -0.320 0.74890
Gendermale 7.713e-01 2.163e+00 3.825e-01 2.017 0.04375 *
Hypertension.compositeno -9.429e-01 3.895e-01 7.405e-01 -1.273 0.20287
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes 9.828e-02 1.103e+00 3.449e-01 0.285 0.77568
SmokerCurrentno -6.301e-01 5.326e-01 3.087e-01 -2.041 0.04125 *
SmokerCurrentyes NA NA 0.000e+00 NA NA
Med.Statin.LLDno -4.514e-02 9.559e-01 3.864e-01 -0.117 0.90701
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 2.968e-02 1.030e+00 4.891e-01 0.061 0.95161
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD -1.446e-02 9.856e-01 8.132e-03 -1.778 0.07534 .
BMI -1.959e-02 9.806e-01 4.266e-02 -0.459 0.64600
CAD_history 1.013e+00 2.755e+00 3.177e-01 3.190 0.00142 **
Stroke_history 3.953e-02 1.040e+00 3.279e-01 0.121 0.90406
Peripheral.interv 3.587e-01 1.431e+00 3.264e-01 1.099 0.27180
stenose0-49% -1.820e+01 1.243e-08 1.223e+04 -0.001 0.99881
stenose50-70% -1.815e+01 1.307e-08 3.044e+03 -0.006 0.99524
stenose70-90% -1.130e+00 3.229e-01 1.061e+00 -1.066 0.28657
stenose90-99% -1.243e+00 2.886e-01 1.065e+00 -1.167 0.24317
stenose100% (Occlusion) -1.839e+01 1.031e-08 9.618e+03 -0.002 0.99847
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% -1.816e+01 1.293e-08 1.466e+04 -0.001 0.99901
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
hsCRP_plasma 1.645e-04 1.000e+00 9.860e-04 0.167 0.86752
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00104,3.34148] 1.156e+00 8.648e-01 0.64415 2.0758
Age 9.937e-01 1.006e+00 0.95577 1.0331
Gendermale 2.163e+00 4.624e-01 1.02186 4.5765
Hypertension.compositeno 3.895e-01 2.567e+00 0.09125 1.6626
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 1.103e+00 9.064e-01 0.56119 2.1690
SmokerCurrentno 5.326e-01 1.878e+00 0.29080 0.9753
SmokerCurrentyes NA NA NA NA
Med.Statin.LLDno 9.559e-01 1.046e+00 0.44820 2.0385
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 1.030e+00 9.708e-01 0.39499 2.6865
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 9.856e-01 1.015e+00 0.97006 1.0015
BMI 9.806e-01 1.020e+00 0.90195 1.0661
CAD_history 2.755e+00 3.630e-01 1.47801 5.1341
Stroke_history 1.040e+00 9.612e-01 0.54707 1.9783
Peripheral.interv 1.431e+00 6.986e-01 0.75500 2.7140
stenose0-49% 1.243e-08 8.046e+07 0.00000 Inf
stenose50-70% 1.307e-08 7.652e+07 0.00000 Inf
stenose70-90% 3.229e-01 3.097e+00 0.04037 2.5823
stenose90-99% 2.886e-01 3.465e+00 0.03579 2.3264
stenose100% (Occlusion) 1.031e-08 9.704e+07 0.00000 Inf
stenoseNA NA NA NA NA
stenose50-99% 1.293e-08 7.734e+07 0.00000 Inf
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
hsCRP_plasma 1.000e+00 9.998e-01 0.99823 1.0021
Concordance= 0.748 (se = 0.035 )
Likelihood ratio test= 38.67 on 20 df, p=0.007
Wald test = 24.85 on 20 df, p=0.2
Score (logrank) test = 36.62 on 20 df, p=0.01
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_pg_ug_2015_rank ' and its association to ' epcoronary.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epcoronary.3years
Protein...................: MCP1_pg_ug_2015_rank
Effect size...............: 0.145257
Standard error............: 0.298514
Odds ratio (effect size)..: 1.156
Lower 95% CI..............: 0.644
Upper 95% CI..............: 2.076
T-value...................: 0.486599
P-value...................: 0.6265423
Sample size in model......: 642
Number of events..........: 48
* Analyzing the effect of plaque proteins on [epcvdeath.3years].
- creating temporary SE for this work.
- making a 'Surv' object and adding this to temporary dataframe.
- making strata of each of the plaque proteins and start survival analysis.
> processing [IL6_rank]; 1 out of 5 proteins.
> cross tabulation of IL6_rank-stratum.
[-1.48329,0.00474) [ 0.00474,3.10694]
265 264
> fitting the model for IL6_rank-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + hsCRP_plasma, data = TEMP.DF)
n= 268, number of events= 16
(2120 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00474,3.10694] -5.623e-01 5.699e-01 5.176e-01 -1.086 0.27738
Age 9.014e-02 1.094e+00 3.751e-02 2.403 0.01626 *
Gendermale 5.128e-01 1.670e+00 6.414e-01 0.800 0.42399
Hypertension.compositeno -1.724e+01 3.243e-08 3.798e+03 -0.005 0.99638
Hypertension.compositeyes 0.000e+00 1.000e+00 3.798e+03 0.000 1.00000
DiabetesStatusDiabetes 1.099e+00 3.002e+00 5.398e-01 2.036 0.04174 *
SmokerCurrentno -8.228e-01 4.392e-01 5.015e-01 -1.641 0.10088
SmokerCurrentyes 0.000e+00 1.000e+00 5.015e-01 0.000 1.00000
Med.Statin.LLDno 5.213e-01 1.684e+00 5.187e-01 1.005 0.31487
Med.Statin.LLDyes 0.000e+00 1.000e+00 5.187e-01 0.000 1.00000
Med.all.antiplateletno -2.972e-01 7.429e-01 1.033e+00 -0.288 0.77362
Med.all.antiplateletyes 0.000e+00 1.000e+00 1.033e+00 0.000 1.00000
GFR_MDRD 3.542e-03 1.004e+00 1.385e-02 0.256 0.79815
BMI 2.457e-02 1.025e+00 6.104e-02 0.403 0.68731
CAD_history 8.404e-01 2.317e+00 5.006e-01 1.679 0.09319 .
Stroke_history 8.332e-01 2.301e+00 5.050e-01 1.650 0.09897 .
Peripheral.interv 2.525e-01 1.287e+00 6.407e-01 0.394 0.69348
stenose0-49% -1.964e+00 1.403e-01 2.902e+04 0.000 0.99995
stenose50-70% 1.189e+00 3.285e+00 9.652e+03 0.000 0.99990
stenose70-90% 1.611e+01 9.915e+06 5.175e-01 31.127 < 2e-16 ***
stenose90-99% 1.550e+01 5.394e+06 5.175e-01 29.951 < 2e-16 ***
stenose100% (Occlusion) 0.000e+00 1.000e+00 1.187e+04 0.000 1.00000
stenoseNA 0.000e+00 1.000e+00 0.000e+00 NA NA
stenose50-99% 0.000e+00 1.000e+00 0.000e+00 NA NA
stenose70-99% 0.000e+00 1.000e+00 0.000e+00 NA NA
stenose99 0.000e+00 1.000e+00 0.000e+00 NA NA
hsCRP_plasma 2.424e-03 1.002e+00 7.772e-04 3.119 0.00182 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00474,3.10694] 5.699e-01 1.755e+00 2.066e-01 1.572e+00
Age 1.094e+00 9.138e-01 1.017e+00 1.178e+00
Gendermale 1.670e+00 5.988e-01 4.751e-01 5.870e+00
Hypertension.compositeno 3.243e-08 3.084e+07 0.000e+00 Inf
Hypertension.compositeyes 1.000e+00 1.000e+00 0.000e+00 Inf
DiabetesStatusDiabetes 3.002e+00 3.331e-01 1.042e+00 8.648e+00
SmokerCurrentno 4.392e-01 2.277e+00 1.643e-01 1.174e+00
SmokerCurrentyes 1.000e+00 1.000e+00 3.742e-01 2.672e+00
Med.Statin.LLDno 1.684e+00 5.937e-01 6.094e-01 4.655e+00
Med.Statin.LLDyes 1.000e+00 1.000e+00 3.618e-01 2.764e+00
Med.all.antiplateletno 7.429e-01 1.346e+00 9.804e-02 5.629e+00
Med.all.antiplateletyes 1.000e+00 1.000e+00 1.320e-01 7.577e+00
GFR_MDRD 1.004e+00 9.965e-01 9.767e-01 1.031e+00
BMI 1.025e+00 9.757e-01 9.093e-01 1.155e+00
CAD_history 2.317e+00 4.315e-01 8.687e-01 6.182e+00
Stroke_history 2.301e+00 4.346e-01 8.550e-01 6.191e+00
Peripheral.interv 1.287e+00 7.768e-01 3.667e-01 4.519e+00
stenose0-49% 1.403e-01 7.126e+00 0.000e+00 Inf
stenose50-70% 3.285e+00 3.044e-01 0.000e+00 Inf
stenose70-90% 9.915e+06 1.009e-07 3.596e+06 2.734e+07
stenose90-99% 5.394e+06 1.854e-07 1.956e+06 1.487e+07
stenose100% (Occlusion) 1.000e+00 1.000e+00 0.000e+00 Inf
stenoseNA 1.000e+00 1.000e+00 1.000e+00 1.000e+00
stenose50-99% 1.000e+00 1.000e+00 1.000e+00 1.000e+00
stenose70-99% 1.000e+00 1.000e+00 1.000e+00 1.000e+00
stenose99 1.000e+00 1.000e+00 1.000e+00 1.000e+00
hsCRP_plasma 1.002e+00 9.976e-01 1.001e+00 1.004e+00
Concordance= 0.832 (se = 0.048 )
Likelihood ratio test= 28.19 on 27 df, p=0.4
Wald test = 1882 on 27 df, p=<2e-16
Score (logrank) test = 46.33 on 27 df, p=0.01
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' IL6_rank ' and its association to ' epcvdeath.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epcvdeath.3years
Protein...................: IL6_rank
Effect size...............: -0.562254
Standard error............: 0.517624
Odds ratio (effect size)..: 0.57
Lower 95% CI..............: 0.207
Upper 95% CI..............: 1.572
T-value...................: -1.08622
P-value...................: 0.2773817
Sample size in model......: 268
Number of events..........: 16
> processing [MCP1_rank]; 2 out of 5 proteins.
> cross tabulation of MCP1_rank-stratum.
[-2.41053,0.00444) [ 0.00444,3.12635]
283 282
> fitting the model for MCP1_rank-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + hsCRP_plasma, data = TEMP.DF)
n= 282, number of events= 17
(2106 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00444,3.12635] -3.478e-01 7.062e-01 4.862e-01 -0.715 0.47442
Age 9.615e-02 1.101e+00 3.755e-02 2.561 0.01044 *
Gendermale 3.885e-01 1.475e+00 6.366e-01 0.610 0.54174
Hypertension.compositeno -1.720e+01 3.376e-08 3.583e+03 -0.005 0.99617
Hypertension.compositeyes 0.000e+00 1.000e+00 3.583e+03 0.000 1.00000
DiabetesStatusDiabetes 9.760e-01 2.654e+00 5.326e-01 1.832 0.06689 .
SmokerCurrentno -9.194e-01 3.988e-01 4.867e-01 -1.889 0.05889 .
SmokerCurrentyes 0.000e+00 1.000e+00 4.867e-01 0.000 1.00000
Med.Statin.LLDno 4.881e-01 1.629e+00 5.090e-01 0.959 0.33763
Med.Statin.LLDyes 0.000e+00 1.000e+00 5.090e-01 0.000 1.00000
Med.all.antiplateletno -7.568e-01 4.691e-01 1.031e+00 -0.734 0.46295
Med.all.antiplateletyes 0.000e+00 1.000e+00 1.031e+00 0.000 1.00000
GFR_MDRD -1.092e-02 9.891e-01 1.325e-02 -0.824 0.40988
BMI 6.871e-04 1.001e+00 5.983e-02 0.011 0.99084
CAD_history 4.382e-01 1.550e+00 4.932e-01 0.888 0.37430
Stroke_history 7.243e-01 2.063e+00 4.866e-01 1.488 0.13665
Peripheral.interv 5.948e-01 1.813e+00 5.720e-01 1.040 0.29844
stenose0-49% -2.305e+00 9.975e-02 2.768e+04 0.000 0.99993
stenose50-70% 1.434e+00 4.195e+00 9.963e+03 0.000 0.99989
stenose70-90% 1.629e+01 1.184e+07 4.935e-01 33.003 < 2e-16 ***
stenose90-99% 1.545e+01 5.152e+06 4.935e-01 31.317 < 2e-16 ***
stenose100% (Occlusion) 0.000e+00 1.000e+00 1.157e+04 0.000 1.00000
stenoseNA 0.000e+00 1.000e+00 0.000e+00 NA NA
stenose50-99% 0.000e+00 1.000e+00 0.000e+00 NA NA
stenose70-99% 0.000e+00 1.000e+00 0.000e+00 NA NA
stenose99 0.000e+00 1.000e+00 0.000e+00 NA NA
hsCRP_plasma 2.536e-03 1.003e+00 7.812e-04 3.246 0.00117 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00444,3.12635] 7.062e-01 1.416e+00 2.723e-01 1.832e+00
Age 1.101e+00 9.083e-01 1.023e+00 1.185e+00
Gendermale 1.475e+00 6.781e-01 4.235e-01 5.136e+00
Hypertension.compositeno 3.376e-08 2.962e+07 0.000e+00 Inf
Hypertension.compositeyes 1.000e+00 1.000e+00 0.000e+00 Inf
DiabetesStatusDiabetes 2.654e+00 3.768e-01 9.343e-01 7.538e+00
SmokerCurrentno 3.988e-01 2.508e+00 1.536e-01 1.035e+00
SmokerCurrentyes 1.000e+00 1.000e+00 3.852e-01 2.596e+00
Med.Statin.LLDno 1.629e+00 6.138e-01 6.007e-01 4.418e+00
Med.Statin.LLDyes 1.000e+00 1.000e+00 3.687e-01 2.712e+00
Med.all.antiplateletno 4.691e-01 2.132e+00 6.217e-02 3.540e+00
Med.all.antiplateletyes 1.000e+00 1.000e+00 1.325e-01 7.546e+00
GFR_MDRD 9.891e-01 1.011e+00 9.638e-01 1.015e+00
BMI 1.001e+00 9.993e-01 8.900e-01 1.125e+00
CAD_history 1.550e+00 6.452e-01 5.895e-01 4.075e+00
Stroke_history 2.063e+00 4.847e-01 7.949e-01 5.355e+00
Peripheral.interv 1.813e+00 5.517e-01 5.908e-01 5.561e+00
stenose0-49% 9.975e-02 1.003e+01 0.000e+00 Inf
stenose50-70% 4.195e+00 2.384e-01 0.000e+00 Inf
stenose70-90% 1.184e+07 8.449e-08 4.499e+06 3.114e+07
stenose90-99% 5.152e+06 1.941e-07 1.958e+06 1.355e+07
stenose100% (Occlusion) 1.000e+00 1.000e+00 0.000e+00 Inf
stenoseNA 1.000e+00 1.000e+00 1.000e+00 1.000e+00
stenose50-99% 1.000e+00 1.000e+00 1.000e+00 1.000e+00
stenose70-99% 1.000e+00 1.000e+00 1.000e+00 1.000e+00
stenose99 1.000e+00 1.000e+00 1.000e+00 1.000e+00
hsCRP_plasma 1.003e+00 9.975e-01 1.001e+00 1.004e+00
Concordance= 0.828 (se = 0.045 )
Likelihood ratio test= 29.01 on 27 df, p=0.4
Wald test = 2090 on 27 df, p=<2e-16
Score (logrank) test = 44.48 on 27 df, p=0.02
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_rank ' and its association to ' epcvdeath.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epcvdeath.3years
Protein...................: MCP1_rank
Effect size...............: -0.347814
Standard error............: 0.486247
Odds ratio (effect size)..: 0.706
Lower 95% CI..............: 0.272
Upper 95% CI..............: 1.832
T-value...................: -0.715304
P-value...................: 0.4744211
Sample size in model......: 282
Number of events..........: 17
> processing [IL6_pg_ug_2015_rank]; 3 out of 5 proteins.
> cross tabulation of IL6_pg_ug_2015_rank-stratum.
[-3.33061,0.00109) [ 0.00109,3.33061]
577 577
> fitting the model for IL6_pg_ug_2015_rank-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + hsCRP_plasma, data = TEMP.DF)
n= 618, number of events= 25
(1770 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00109,3.33061] 5.868e-01 1.798e+00 4.349e-01 1.349 0.17728
Age 8.576e-02 1.090e+00 3.129e-02 2.741 0.00613 **
Gendermale 1.461e+00 4.311e+00 6.332e-01 2.308 0.02102 *
Hypertension.compositeno -1.851e+01 9.172e-09 6.011e+03 -0.003 0.99754
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes 3.247e-01 1.384e+00 4.744e-01 0.685 0.49364
SmokerCurrentno -7.793e-01 4.587e-01 4.542e-01 -1.716 0.08620 .
SmokerCurrentyes NA NA 0.000e+00 NA NA
Med.Statin.LLDno -1.421e-01 8.676e-01 5.120e-01 -0.277 0.78143
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 1.082e+00 2.950e+00 5.042e-01 2.145 0.03192 *
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD -2.560e-02 9.747e-01 1.086e-02 -2.358 0.01836 *
BMI 5.713e-02 1.059e+00 6.046e-02 0.945 0.34476
CAD_history 3.951e-01 1.485e+00 4.308e-01 0.917 0.35911
Stroke_history -2.076e-01 8.126e-01 4.592e-01 -0.452 0.65126
Peripheral.interv 6.257e-01 1.870e+00 5.055e-01 1.238 0.21575
stenose0-49% -2.006e+01 1.936e-09 3.815e+04 -0.001 0.99958
stenose50-70% -2.110e+00 1.213e-01 1.507e+00 -1.400 0.16148
stenose70-90% -1.796e+00 1.660e-01 1.153e+00 -1.558 0.11929
stenose90-99% -1.789e+00 1.671e-01 1.147e+00 -1.561 0.11862
stenose100% (Occlusion) -1.950e+01 3.397e-09 3.254e+04 -0.001 0.99952
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% NA NA 0.000e+00 NA NA
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
hsCRP_plasma 1.842e-03 1.002e+00 8.093e-04 2.276 0.02284 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00109,3.33061] 1.798e+00 5.561e-01 0.766703 4.2175
Age 1.090e+00 9.178e-01 1.024736 1.1584
Gendermale 4.311e+00 2.319e-01 1.246294 14.9145
Hypertension.compositeno 9.172e-09 1.090e+08 0.000000 Inf
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 1.384e+00 7.227e-01 0.546052 3.5061
SmokerCurrentno 4.587e-01 2.180e+00 0.188334 1.1173
SmokerCurrentyes NA NA NA NA
Med.Statin.LLDno 8.676e-01 1.153e+00 0.318014 2.3667
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 2.950e+00 3.390e-01 1.098017 7.9250
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 9.747e-01 1.026e+00 0.954200 0.9957
BMI 1.059e+00 9.445e-01 0.940466 1.1920
CAD_history 1.485e+00 6.736e-01 0.638076 3.4537
Stroke_history 8.126e-01 1.231e+00 0.330342 1.9987
Peripheral.interv 1.870e+00 5.349e-01 0.694219 5.0349
stenose0-49% 1.936e-09 5.166e+08 0.000000 Inf
stenose50-70% 1.213e-01 8.246e+00 0.006326 2.3247
stenose70-90% 1.660e-01 6.023e+00 0.017338 1.5898
stenose90-99% 1.671e-01 5.986e+00 0.017657 1.5808
stenose100% (Occlusion) 3.397e-09 2.944e+08 0.000000 Inf
stenoseNA NA NA NA NA
stenose50-99% NA NA NA NA
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
hsCRP_plasma 1.002e+00 9.982e-01 1.000256 1.0034
Concordance= 0.845 (se = 0.032 )
Likelihood ratio test= 49.5 on 19 df, p=2e-04
Wald test = 18.65 on 19 df, p=0.5
Score (logrank) test = 48.91 on 19 df, p=2e-04
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' IL6_pg_ug_2015_rank ' and its association to ' epcvdeath.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epcvdeath.3years
Protein...................: IL6_pg_ug_2015_rank
Effect size...............: 0.586793
Standard error............: 0.434931
Odds ratio (effect size)..: 1.798
Lower 95% CI..............: 0.767
Upper 95% CI..............: 4.218
T-value...................: 1.349163
P-value...................: 0.1772846
Sample size in model......: 618
Number of events..........: 25
> processing [IL6R_pg_ug_2015_rank]; 4 out of 5 proteins.
> cross tabulation of IL6R_pg_ug_2015_rank-stratum.
[-3.33109,0.00108) [ 0.00108,3.33109]
578 578
> fitting the model for IL6R_pg_ug_2015_rank-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + hsCRP_plasma, data = TEMP.DF)
n= 623, number of events= 25
(1765 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00108,3.33109] 9.200e-01 2.509e+00 4.915e-01 1.872 0.06121 .
Age 8.762e-02 1.092e+00 3.174e-02 2.761 0.00577 **
Gendermale 1.325e+00 3.763e+00 6.336e-01 2.092 0.03647 *
Hypertension.compositeno -1.845e+01 9.731e-09 6.057e+03 -0.003 0.99757
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes 4.263e-01 1.532e+00 4.754e-01 0.897 0.36993
SmokerCurrentno -6.118e-01 5.424e-01 4.623e-01 -1.323 0.18576
SmokerCurrentyes NA NA 0.000e+00 NA NA
Med.Statin.LLDno -2.450e-01 7.827e-01 5.154e-01 -0.475 0.63449
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 9.711e-01 2.641e+00 5.053e-01 1.922 0.05464 .
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD -2.516e-02 9.752e-01 1.078e-02 -2.333 0.01963 *
BMI 5.238e-02 1.054e+00 6.174e-02 0.848 0.39623
CAD_history 4.604e-01 1.585e+00 4.289e-01 1.073 0.28310
Stroke_history -2.589e-01 7.719e-01 4.621e-01 -0.560 0.57534
Peripheral.interv 4.685e-01 1.598e+00 5.021e-01 0.933 0.35079
stenose0-49% -1.988e+01 2.330e-09 4.926e+04 0.000 0.99968
stenose50-70% -2.693e+00 6.767e-02 1.568e+00 -1.717 0.08590 .
stenose70-90% -2.176e+00 1.135e-01 1.157e+00 -1.881 0.05997 .
stenose90-99% -2.298e+00 1.005e-01 1.168e+00 -1.967 0.04921 *
stenose100% (Occlusion) -1.992e+01 2.244e-09 3.186e+04 -0.001 0.99950
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% -1.974e+01 2.667e-09 4.794e+04 0.000 0.99967
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
hsCRP_plasma 1.959e-03 1.002e+00 8.471e-04 2.313 0.02073 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00108,3.33109] 2.509e+00 3.985e-01 0.957679 6.5752
Age 1.092e+00 9.161e-01 1.025738 1.1616
Gendermale 3.763e+00 2.657e-01 1.087054 13.0288
Hypertension.compositeno 9.731e-09 1.028e+08 0.000000 Inf
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 1.532e+00 6.530e-01 0.603190 3.8885
SmokerCurrentno 5.424e-01 1.844e+00 0.219173 1.3423
SmokerCurrentyes NA NA NA NA
Med.Statin.LLDno 7.827e-01 1.278e+00 0.285005 2.1494
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 2.641e+00 3.787e-01 0.980860 7.1100
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 9.752e-01 1.025e+00 0.954767 0.9960
BMI 1.054e+00 9.490e-01 0.933673 1.1893
CAD_history 1.585e+00 6.310e-01 0.683667 3.6734
Stroke_history 7.719e-01 1.295e+00 0.312061 1.9095
Peripheral.interv 1.598e+00 6.259e-01 0.597114 4.2747
stenose0-49% 2.330e-09 4.293e+08 0.000000 Inf
stenose50-70% 6.767e-02 1.478e+01 0.003131 1.4628
stenose70-90% 1.135e-01 8.807e+00 0.011768 1.0956
stenose90-99% 1.005e-01 9.950e+00 0.010182 0.9921
stenose100% (Occlusion) 2.244e-09 4.457e+08 0.000000 Inf
stenoseNA NA NA NA NA
stenose50-99% 2.667e-09 3.749e+08 0.000000 Inf
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
hsCRP_plasma 1.002e+00 9.980e-01 1.000299 1.0036
Concordance= 0.847 (se = 0.036 )
Likelihood ratio test= 51.36 on 20 df, p=1e-04
Wald test = 19.44 on 20 df, p=0.5
Score (logrank) test = 52.12 on 20 df, p=1e-04
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' IL6R_pg_ug_2015_rank ' and its association to ' epcvdeath.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epcvdeath.3years
Protein...................: IL6R_pg_ug_2015_rank
Effect size...............: 0.920029
Standard error............: 0.491474
Odds ratio (effect size)..: 2.509
Lower 95% CI..............: 0.958
Upper 95% CI..............: 6.575
T-value...................: 1.871978
P-value...................: 0.06120959
Sample size in model......: 623
Number of events..........: 25
> processing [MCP1_pg_ug_2015_rank]; 5 out of 5 proteins.
> cross tabulation of MCP1_pg_ug_2015_rank-stratum.
[-3.34148,0.00104) [ 0.00104,3.34148]
600 600
> fitting the model for MCP1_pg_ug_2015_rank-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + hsCRP_plasma, data = TEMP.DF)
n= 642, number of events= 25
(1746 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00104,3.34148] -1.961e-01 8.219e-01 4.254e-01 -0.461 0.64476
Age 8.139e-02 1.085e+00 3.150e-02 2.584 0.00977 **
Gendermale 1.496e+00 4.463e+00 6.350e-01 2.355 0.01850 *
Hypertension.compositeno -1.747e+01 2.583e-08 3.750e+03 -0.005 0.99628
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes 3.497e-01 1.419e+00 4.764e-01 0.734 0.46292
SmokerCurrentno -7.776e-01 4.595e-01 4.567e-01 -1.703 0.08859 .
SmokerCurrentyes NA NA 0.000e+00 NA NA
Med.Statin.LLDno -3.699e-02 9.637e-01 5.069e-01 -0.073 0.94182
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 1.008e+00 2.740e+00 5.031e-01 2.003 0.04513 *
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD -2.859e-02 9.718e-01 1.106e-02 -2.586 0.00972 **
BMI 5.917e-02 1.061e+00 5.925e-02 0.999 0.31795
CAD_history 4.010e-01 1.493e+00 4.292e-01 0.934 0.35016
Stroke_history -1.402e-01 8.692e-01 4.647e-01 -0.302 0.76288
Peripheral.interv 5.585e-01 1.748e+00 5.050e-01 1.106 0.26872
stenose0-49% -1.954e+01 3.273e-09 2.318e+04 -0.001 0.99933
stenose50-70% -2.121e+00 1.199e-01 1.503e+00 -1.411 0.15838
stenose70-90% -1.974e+00 1.390e-01 1.154e+00 -1.710 0.08718 .
stenose90-99% -1.993e+00 1.362e-01 1.160e+00 -1.718 0.08581 .
stenose100% (Occlusion) -1.891e+01 6.112e-09 2.011e+04 -0.001 0.99925
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% -1.890e+01 6.179e-09 3.012e+04 -0.001 0.99950
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
hsCRP_plasma 1.954e-03 1.002e+00 8.209e-04 2.380 0.01730 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00104,3.34148] 8.219e-01 1.217e+00 0.357032 1.8920
Age 1.085e+00 9.218e-01 1.019845 1.1539
Gendermale 4.463e+00 2.241e-01 1.285442 15.4930
Hypertension.compositeno 2.583e-08 3.871e+07 0.000000 Inf
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 1.419e+00 7.049e-01 0.557642 3.6091
SmokerCurrentno 4.595e-01 2.176e+00 0.187748 1.1246
SmokerCurrentyes NA NA NA NA
Med.Statin.LLDno 9.637e-01 1.038e+00 0.356816 2.6027
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 2.740e+00 3.650e-01 1.022133 7.3447
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 9.718e-01 1.029e+00 0.950986 0.9931
BMI 1.061e+00 9.425e-01 0.944634 1.1916
CAD_history 1.493e+00 6.697e-01 0.643894 3.4632
Stroke_history 8.692e-01 1.150e+00 0.349605 2.1610
Peripheral.interv 1.748e+00 5.721e-01 0.649724 4.7030
stenose0-49% 3.273e-09 3.055e+08 0.000000 Inf
stenose50-70% 1.199e-01 8.337e+00 0.006298 2.2843
stenose70-90% 1.390e-01 7.196e+00 0.014479 1.3336
stenose90-99% 1.362e-01 7.341e+00 0.014014 1.3242
stenose100% (Occlusion) 6.112e-09 1.636e+08 0.000000 Inf
stenoseNA NA NA NA NA
stenose50-99% 6.179e-09 1.618e+08 0.000000 Inf
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
hsCRP_plasma 1.002e+00 9.980e-01 1.000345 1.0036
Concordance= 0.833 (se = 0.033 )
Likelihood ratio test= 48.16 on 20 df, p=4e-04
Wald test = 16.04 on 20 df, p=0.7
Score (logrank) test = 47.77 on 20 df, p=5e-04
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_pg_ug_2015_rank ' and its association to ' epcvdeath.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epcvdeath.3years
Protein...................: MCP1_pg_ug_2015_rank
Effect size...............: -0.196136
Standard error............: 0.425413
Odds ratio (effect size)..: 0.822
Lower 95% CI..............: 0.357
Upper 95% CI..............: 1.892
T-value...................: -0.461049
P-value...................: 0.6447633
Sample size in model......: 642
Number of events..........: 25 
cat("- Edit the column names...\n")- Edit the column names...colnames(COX.results) = c("Dataset", "Outcome", "CpG",
"Beta", "s.e.m.",
"HR", "low95CI", "up95CI",
"Z-value", "P-value", "SampleSize", "N_events")
cat("- Correct the variable types...\n")- Correct the variable types...COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)
AEDB.CEA.COX.results <- COX.results
# Save the data
cat("- Writing results to Excel-file...\n")- Writing results to Excel-file...head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AEDB.CEA.COX.results,
file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Cox.2G.MODEL3.xlsx"),
creator = "Sander W. van der Laan",
sheetName = "Results", headerStyle = head.style,
row.names = FALSE, col.names = TRUE, overwrite = TRUE)
# Removing intermediates
cat("- Removing intermediate files...\n")- Removing intermediate files...rm(TEMP.DF, protein, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AEDB.CEA.COX.results)
rm(head.style)object 'head.style' not foundMODEL 4
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))
# Looping over each protein/endpoint/time combination
for (i in 1:length(times)){
eptime = times[i]
ep = endpoints[i]
cat(paste0("* Analyzing the effect of plaque proteins on [",ep,"].\n"))
cat(" - creating temporary SE for this work.\n")
TEMP.DF = as.data.frame(AEDB.CEA)
cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
TEMP.DF$event <- as.integer(TEMP.DF[,ep])
#as.integer(TEMP.DF[,ep] == "Excluded")
TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
cat(" - making strata of each of the plaque proteins and start survival analysis.\n")
for (protein in 1:length(TRAITS.PROTEIN.RANK)){
cat(paste0(" > processing [",TRAITS.PROTEIN.RANK[protein],"]; ",protein," out of ",length(TRAITS.PROTEIN.RANK)," proteins.\n"))
# splitting into two groups
TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]] <- cut2(TEMP.DF[,TRAITS.PROTEIN.RANK[protein]], g = 2)
cat(paste0(" > cross tabulation of ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
show(table(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]))
cat(paste0("\n > fitting the model for ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
fit <- survfit(as.formula(paste0("y ~ ", TRAITS.PROTEIN.RANK[protein])), data = TEMP.DF)
cat(paste0("\n > make a Kaplan-Meier-shizzle...\n"))
# make Kaplan-Meier curve and save it
show(ggsurvplot(fit, data = TEMP.DF,
palette = c("#DB003F", "#1290D9"),
# palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
linetype = c(1,2),
# linetype = c(1,2,3,4),
# conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
pval = FALSE, pval.method = FALSE, pval.size = 4,
risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
censor = FALSE,
legend = "right",
legend.title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
legend.labs = c("low", "high"),
title = paste0("Risk of ",ep,""), xlab = "Time [years]", font.main = c(16, "bold", "black")))
dev.copy2pdf(file = paste0(COX_loc,"/",
Today,".AEDB.CEA.survival.",ep,".2G.",
TRAITS.PROTEIN.RANK[protein],".pdf"), width = 12, height = 10, onefile = FALSE)
cat(paste0("\n > perform the Cox-regression fashizzle and plot it...\n"))
### Do Cox-regression and plot it
### MODEL 4 same to model 2 with additional adjustment for IL6 levels in the plaque
cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]+Age + Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + CAD_history + Stroke_history + Peripheral.interv + stenose + IL6_pg_ug_2015_rank, data = TEMP.DF)
coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]])+Age + Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + CAD_history + Stroke_history + Peripheral.interv + stenose + IL6_pg_ug_2015_rank, data = TEMP.DF)
plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
# ylim = c(0.2, 1), xlim = c(0,3), col = c("#595A5C", "#DB003F", "#1290D9"),
ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
lty = c(1,2), lwd = 2,
ylab = "Suvival probability", xlab = "FU time [years]",
mark.time = FALSE, axes = FALSE, bty = "n")
legend("topright",
c("low", "high"),
title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
col = c("#DB003F", "#1290D9"),
lty = c(1,2), lwd = 2,
bty = "n")
axis(side = 1, at = seq(0, 3, by = 1))
axis(side = 2, at = seq(0, 1, by = 0.2))
dev.copy2pdf(file = paste0(COX_loc,"/",
Today,".AEDB.CEA.Cox.",ep,".2G.",
# Today,".AEDB.CEA.Cox.",ep,".4G.",
TRAITS.PROTEIN.RANK[protein],".MODEL4.pdf"), height = 12, width = 10, onefile = TRUE)
show(summary(cox))
cat(paste0("\n > writing the Cox-regression fashizzle to Excel...\n"))
COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
COX.results.TEMP[1,] = COX.STAT(cox, "AEDB.CEA", ep, TRAITS.PROTEIN.RANK[protein])
COX.results = rbind(COX.results, COX.results.TEMP)
}
}* Analyzing the effect of plaque proteins on [epmajor.3years].
- creating temporary SE for this work.
- making a 'Surv' object and adding this to temporary dataframe.
- making strata of each of the plaque proteins and start survival analysis.
> processing [IL6_rank]; 1 out of 5 proteins.
> cross tabulation of IL6_rank-stratum.
[-1.48329,0.00474) [ 0.00474,3.10694]
265 264
> fitting the model for IL6_rank-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + IL6_pg_ug_2015_rank, data = TEMP.DF)
n= 368, number of events= 50
(2020 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00474,3.10694] -9.203e-02 9.121e-01 3.008e-01 -0.306 0.75961
Age 6.216e-02 1.064e+00 2.126e-02 2.924 0.00345 **
Gendermale 1.064e+00 2.897e+00 4.251e-01 2.503 0.01233 *
Hypertension.compositeno -7.227e-01 4.854e-01 6.297e-01 -1.148 0.25112
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes 5.258e-01 1.692e+00 3.356e-01 1.567 0.11714
SmokerCurrentno -5.752e-01 5.626e-01 3.027e-01 -1.900 0.05740 .
SmokerCurrentyes NA NA 0.000e+00 NA NA
Med.Statin.LLDno 3.211e-01 1.379e+00 3.247e-01 0.989 0.32266
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 1.783e-01 1.195e+00 4.485e-01 0.398 0.69099
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD -9.516e-03 9.905e-01 7.737e-03 -1.230 0.21874
BMI 9.862e-03 1.010e+00 4.458e-02 0.221 0.82492
CAD_history 5.590e-01 1.749e+00 3.084e-01 1.813 0.06985 .
Stroke_history 2.059e-02 1.021e+00 3.090e-01 0.067 0.94687
Peripheral.interv 1.198e-01 1.127e+00 3.666e-01 0.327 0.74372
stenose0-49% -1.676e+01 5.238e-08 3.616e+03 -0.005 0.99630
stenose50-70% -1.716e+00 1.798e-01 1.537e+00 -1.116 0.26426
stenose70-90% -1.110e+00 3.294e-01 1.124e+00 -0.987 0.32343
stenose90-99% -1.331e+00 2.643e-01 1.141e+00 -1.166 0.24357
stenose100% (Occlusion) NA NA 0.000e+00 NA NA
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% NA NA 0.000e+00 NA NA
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
IL6_pg_ug_2015_rank 1.331e-01 1.142e+00 1.516e-01 0.878 0.37985
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00474,3.10694] 9.121e-01 1.096e+00 0.505860 1.645
Age 1.064e+00 9.397e-01 1.020708 1.109
Gendermale 2.897e+00 3.451e-01 1.259434 6.665
Hypertension.compositeno 4.854e-01 2.060e+00 0.141289 1.668
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 1.692e+00 5.911e-01 0.876431 3.266
SmokerCurrentno 5.626e-01 1.777e+00 0.310871 1.018
SmokerCurrentyes NA NA NA NA
Med.Statin.LLDno 1.379e+00 7.253e-01 0.729599 2.605
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 1.195e+00 8.367e-01 0.496232 2.878
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 9.905e-01 1.010e+00 0.975621 1.006
BMI 1.010e+00 9.902e-01 0.925414 1.102
CAD_history 1.749e+00 5.718e-01 0.955666 3.201
Stroke_history 1.021e+00 9.796e-01 0.557041 1.871
Peripheral.interv 1.127e+00 8.871e-01 0.549588 2.312
stenose0-49% 5.238e-08 1.909e+07 0.000000 Inf
stenose50-70% 1.798e-01 5.563e+00 0.008836 3.657
stenose70-90% 3.294e-01 3.035e+00 0.036359 2.985
stenose90-99% 2.643e-01 3.784e+00 0.028233 2.474
stenose100% (Occlusion) NA NA NA NA
stenoseNA NA NA NA NA
stenose50-99% NA NA NA NA
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
IL6_pg_ug_2015_rank 1.142e+00 8.754e-01 0.848765 1.538
Concordance= 0.74 (se = 0.034 )
Likelihood ratio test= 37.52 on 18 df, p=0.004
Wald test = 33.01 on 18 df, p=0.02
Score (logrank) test = 35.91 on 18 df, p=0.007
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' IL6_rank ' and its association to ' epmajor.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epmajor.3years
Protein...................: IL6_rank
Effect size...............: -0.092028
Standard error............: 0.300754
Odds ratio (effect size)..: 0.912
Lower 95% CI..............: 0.506
Upper 95% CI..............: 1.645
T-value...................: -0.30599
P-value...................: 0.759612
Sample size in model......: 368
Number of events..........: 50
> processing [MCP1_rank]; 2 out of 5 proteins.
> cross tabulation of MCP1_rank-stratum.
[-2.41053,0.00444) [ 0.00444,3.12635]
283 282
> fitting the model for MCP1_rank-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + IL6_pg_ug_2015_rank, data = TEMP.DF)
n= 392, number of events= 50
(1996 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00444,3.12635] -5.238e-01 5.923e-01 3.055e-01 -1.715 0.08639 .
Age 5.763e-02 1.059e+00 2.098e-02 2.747 0.00601 **
Gendermale 1.092e+00 2.981e+00 4.240e-01 2.576 0.00999 **
Hypertension.compositeno -7.803e-01 4.583e-01 6.261e-01 -1.246 0.21268
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes 4.063e-01 1.501e+00 3.367e-01 1.207 0.22760
SmokerCurrentno -4.639e-01 6.288e-01 3.079e-01 -1.507 0.13181
SmokerCurrentyes NA NA 0.000e+00 NA NA
Med.Statin.LLDno 4.195e-01 1.521e+00 3.257e-01 1.288 0.19774
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 9.793e-02 1.103e+00 4.530e-01 0.216 0.82883
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD -1.674e-02 9.834e-01 7.946e-03 -2.106 0.03519 *
BMI 2.065e-03 1.002e+00 4.407e-02 0.047 0.96263
CAD_history 3.553e-01 1.427e+00 3.145e-01 1.130 0.25855
Stroke_history 1.127e-01 1.119e+00 3.028e-01 0.372 0.70972
Peripheral.interv 1.911e-01 1.211e+00 3.653e-01 0.523 0.60093
stenose0-49% -1.665e+01 5.854e-08 3.722e+03 -0.004 0.99643
stenose50-70% -1.447e+00 2.353e-01 1.534e+00 -0.943 0.34543
stenose70-90% -1.012e+00 3.634e-01 1.117e+00 -0.907 0.36460
stenose90-99% -1.217e+00 2.961e-01 1.139e+00 -1.068 0.28537
stenose100% (Occlusion) NA NA 0.000e+00 NA NA
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% NA NA 0.000e+00 NA NA
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
IL6_pg_ug_2015_rank 2.158e-01 1.241e+00 1.539e-01 1.402 0.16080
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00444,3.12635] 5.923e-01 1.688e+00 0.32548 1.0778
Age 1.059e+00 9.440e-01 1.01665 1.1038
Gendermale 2.981e+00 3.355e-01 1.29849 6.8430
Hypertension.compositeno 4.583e-01 2.182e+00 0.13432 1.5635
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 1.501e+00 6.661e-01 0.77594 2.9043
SmokerCurrentno 6.288e-01 1.590e+00 0.34393 1.1496
SmokerCurrentyes NA NA NA NA
Med.Statin.LLDno 1.521e+00 6.574e-01 0.80343 2.8804
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 1.103e+00 9.067e-01 0.45391 2.6797
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 9.834e-01 1.017e+00 0.96821 0.9988
BMI 1.002e+00 9.979e-01 0.91914 1.0925
CAD_history 1.427e+00 7.010e-01 0.77023 2.6424
Stroke_history 1.119e+00 8.934e-01 0.61834 2.0261
Peripheral.interv 1.211e+00 8.261e-01 0.59163 2.4769
stenose0-49% 5.854e-08 1.708e+07 0.00000 Inf
stenose50-70% 2.353e-01 4.250e+00 0.01165 4.7531
stenose70-90% 3.634e-01 2.752e+00 0.04072 3.2419
stenose90-99% 2.961e-01 3.377e+00 0.03175 2.7615
stenose100% (Occlusion) NA NA NA NA
stenoseNA NA NA NA NA
stenose50-99% NA NA NA NA
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
IL6_pg_ug_2015_rank 1.241e+00 8.059e-01 0.91776 1.6778
Concordance= 0.753 (se = 0.033 )
Likelihood ratio test= 42.55 on 18 df, p=9e-04
Wald test = 37.59 on 18 df, p=0.004
Score (logrank) test = 40.38 on 18 df, p=0.002
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_rank ' and its association to ' epmajor.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epmajor.3years
Protein...................: MCP1_rank
Effect size...............: -0.523784
Standard error............: 0.305456
Odds ratio (effect size)..: 0.592
Lower 95% CI..............: 0.325
Upper 95% CI..............: 1.078
T-value...................: -1.714758
P-value...................: 0.08638969
Sample size in model......: 392
Number of events..........: 50
> processing [IL6_pg_ug_2015_rank]; 3 out of 5 proteins.
> cross tabulation of IL6_pg_ug_2015_rank-stratum.
[-3.33061,0.00109) [ 0.00109,3.33061]
577 577
> fitting the model for IL6_pg_ug_2015_rank-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + IL6_pg_ug_2015_rank, data = TEMP.DF)
n= 1002, number of events= 114
(1386 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00109,3.33061] 9.097e-02 1.095e+00 1.899e-01 0.479 0.631870
Age 3.856e-02 1.039e+00 1.296e-02 2.975 0.002927 **
Gendermale 5.730e-01 1.774e+00 2.310e-01 2.481 0.013107 *
Hypertension.compositeno -5.174e-01 5.961e-01 3.773e-01 -1.371 0.170285
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes -4.403e-02 9.569e-01 2.233e-01 -0.197 0.843644
SmokerCurrentno -6.023e-01 5.475e-01 2.046e-01 -2.944 0.003240 **
SmokerCurrentyes NA NA 0.000e+00 NA NA
Med.Statin.LLDno 3.369e-01 1.401e+00 2.175e-01 1.549 0.121332
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 4.026e-01 1.496e+00 2.586e-01 1.557 0.119528
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD -1.899e-02 9.812e-01 4.977e-03 -3.815 0.000136 ***
BMI 5.747e-02 1.059e+00 2.597e-02 2.213 0.026905 *
CAD_history 1.411e-01 1.152e+00 2.032e-01 0.695 0.487257
Stroke_history 4.304e-02 1.044e+00 2.028e-01 0.212 0.831900
Peripheral.interv 6.318e-01 1.881e+00 2.183e-01 2.894 0.003802 **
stenose0-49% -1.561e+01 1.659e-07 2.745e+03 -0.006 0.995462
stenose50-70% -8.519e-01 4.266e-01 8.692e-01 -0.980 0.326998
stenose70-90% -2.953e-01 7.443e-01 7.264e-01 -0.406 0.684395
stenose90-99% -2.722e-01 7.617e-01 7.236e-01 -0.376 0.706782
stenose100% (Occlusion) 4.662e-02 1.048e+00 1.239e+00 0.038 0.969996
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% -1.539e+01 2.069e-07 4.208e+03 -0.004 0.997082
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
IL6_pg_ug_2015_rank[ 0.00109,3.33061] NA NA 0.000e+00 NA NA
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00109,3.33061] 1.095e+00 9.130e-01 0.75490 1.5890
Age 1.039e+00 9.622e-01 1.01325 1.0660
Gendermale 1.774e+00 5.638e-01 1.12785 2.7890
Hypertension.compositeno 5.961e-01 1.678e+00 0.28453 1.2487
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 9.569e-01 1.045e+00 0.61778 1.4822
SmokerCurrentno 5.475e-01 1.826e+00 0.36664 0.8176
SmokerCurrentyes NA NA NA NA
Med.Statin.LLDno 1.401e+00 7.140e-01 0.91454 2.1451
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 1.496e+00 6.686e-01 0.90098 2.4831
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 9.812e-01 1.019e+00 0.97167 0.9908
BMI 1.059e+00 9.442e-01 1.00659 1.1145
CAD_history 1.152e+00 8.684e-01 0.77330 1.7150
Stroke_history 1.044e+00 9.579e-01 0.70161 1.5534
Peripheral.interv 1.881e+00 5.316e-01 1.22621 2.8853
stenose0-49% 1.659e-07 6.027e+06 0.00000 Inf
stenose50-70% 4.266e-01 2.344e+00 0.07765 2.3434
stenose70-90% 7.443e-01 1.343e+00 0.17924 3.0909
stenose90-99% 7.617e-01 1.313e+00 0.18446 3.1454
stenose100% (Occlusion) 1.048e+00 9.545e-01 0.09231 11.8918
stenoseNA NA NA NA NA
stenose50-99% 2.069e-07 4.833e+06 0.00000 Inf
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
IL6_pg_ug_2015_rank[ 0.00109,3.33061] NA NA NA NA
Concordance= 0.705 (se = 0.023 )
Likelihood ratio test= 68.36 on 19 df, p=2e-07
Wald test = 38.72 on 19 df, p=0.005
Score (logrank) test = 66.86 on 19 df, p=3e-07
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' IL6_pg_ug_2015_rank ' and its association to ' epmajor.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epmajor.3years
Protein...................: IL6_pg_ug_2015_rank
Effect size...............: 0.090966
Standard error............: 0.189871
Odds ratio (effect size)..: 1.095
Lower 95% CI..............: 0.755
Upper 95% CI..............: 1.589
T-value...................: 0.479096
P-value...................: 0.6318701
Sample size in model......: 1002
Number of events..........: 114
> processing [IL6R_pg_ug_2015_rank]; 4 out of 5 proteins.
> cross tabulation of IL6R_pg_ug_2015_rank-stratum.
[-3.33109,0.00108) [ 0.00108,3.33109]
578 578
> fitting the model for IL6R_pg_ug_2015_rank-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + IL6_pg_ug_2015_rank, data = TEMP.DF)
n= 970, number of events= 113
(1418 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00108,3.33109] 3.455e-01 1.413e+00 2.077e-01 1.663 0.096227 .
Age 3.817e-02 1.039e+00 1.312e-02 2.908 0.003635 **
Gendermale 5.637e-01 1.757e+00 2.340e-01 2.409 0.016010 *
Hypertension.compositeno -5.208e-01 5.941e-01 3.784e-01 -1.376 0.168720
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes -5.564e-02 9.459e-01 2.253e-01 -0.247 0.804978
SmokerCurrentno -5.877e-01 5.556e-01 2.061e-01 -2.851 0.004359 **
SmokerCurrentyes NA NA 0.000e+00 NA NA
Med.Statin.LLDno 2.948e-01 1.343e+00 2.206e-01 1.337 0.181368
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 4.108e-01 1.508e+00 2.591e-01 1.586 0.112845
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD -1.747e-02 9.827e-01 5.018e-03 -3.481 0.000499 ***
BMI 5.835e-02 1.060e+00 2.728e-02 2.139 0.032435 *
CAD_history 1.816e-01 1.199e+00 2.041e-01 0.890 0.373610
Stroke_history 3.289e-02 1.033e+00 2.028e-01 0.162 0.871165
Peripheral.interv 6.233e-01 1.865e+00 2.186e-01 2.851 0.004362 **
stenose0-49% -1.443e+01 5.407e-07 1.940e+03 -0.007 0.994066
stenose50-70% -8.746e-01 4.170e-01 8.691e-01 -1.006 0.314250
stenose70-90% -3.829e-01 6.819e-01 7.286e-01 -0.526 0.599224
stenose90-99% -3.973e-01 6.721e-01 7.273e-01 -0.546 0.584875
stenose100% (Occlusion) -5.011e-02 9.511e-01 1.239e+00 -0.040 0.967735
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% -1.453e+01 4.871e-07 2.498e+03 -0.006 0.995358
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
IL6_pg_ug_2015_rank[ 0.00109,3.33061] -1.395e-02 9.862e-01 1.990e-01 -0.070 0.944120
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00108,3.33109] 1.413e+00 7.079e-01 0.94028 2.1223
Age 1.039e+00 9.626e-01 1.01252 1.0660
Gendermale 1.757e+00 5.691e-01 1.11073 2.7799
Hypertension.compositeno 5.941e-01 1.683e+00 0.28299 1.2471
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 9.459e-01 1.057e+00 0.60818 1.4711
SmokerCurrentno 5.556e-01 1.800e+00 0.37094 0.8322
SmokerCurrentyes NA NA NA NA
Med.Statin.LLDno 1.343e+00 7.447e-01 0.87153 2.0691
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 1.508e+00 6.631e-01 0.90754 2.5059
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 9.827e-01 1.018e+00 0.97307 0.9924
BMI 1.060e+00 9.433e-01 1.00490 1.1183
CAD_history 1.199e+00 8.339e-01 0.80377 1.7890
Stroke_history 1.033e+00 9.676e-01 0.69450 1.5378
Peripheral.interv 1.865e+00 5.362e-01 1.21501 2.8627
stenose0-49% 5.407e-07 1.850e+06 0.00000 Inf
stenose50-70% 4.170e-01 2.398e+00 0.07593 2.2905
stenose70-90% 6.819e-01 1.467e+00 0.16350 2.8438
stenose90-99% 6.721e-01 1.488e+00 0.16157 2.7960
stenose100% (Occlusion) 9.511e-01 1.051e+00 0.08388 10.7852
stenoseNA NA NA NA NA
stenose50-99% 4.871e-07 2.053e+06 0.00000 Inf
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
IL6_pg_ug_2015_rank[ 0.00109,3.33061] 9.862e-01 1.014e+00 0.66771 1.4565
Concordance= 0.706 (se = 0.023 )
Likelihood ratio test= 68.71 on 20 df, p=3e-07
Wald test = 64.7 on 20 df, p=1e-06
Score (logrank) test = 68.67 on 20 df, p=3e-07
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' IL6R_pg_ug_2015_rank ' and its association to ' epmajor.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epmajor.3years
Protein...................: IL6R_pg_ug_2015_rank
Effect size...............: 0.345452
Standard error............: 0.207675
Odds ratio (effect size)..: 1.413
Lower 95% CI..............: 0.94
Upper 95% CI..............: 2.122
T-value...................: 1.663429
P-value...................: 0.09622664
Sample size in model......: 970
Number of events..........: 113
> processing [MCP1_pg_ug_2015_rank]; 5 out of 5 proteins.
> cross tabulation of MCP1_pg_ug_2015_rank-stratum.
[-3.34148,0.00104) [ 0.00104,3.34148]
600 600
> fitting the model for MCP1_pg_ug_2015_rank-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + IL6_pg_ug_2015_rank, data = TEMP.DF)
n= 1001, number of events= 114
(1387 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00104,3.34148] 1.631e-01 1.177e+00 1.990e-01 0.820 0.412212
Age 3.772e-02 1.038e+00 1.299e-02 2.905 0.003673 **
Gendermale 5.672e-01 1.763e+00 2.311e-01 2.454 0.014136 *
Hypertension.compositeno -5.363e-01 5.849e-01 3.781e-01 -1.419 0.156010
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes -4.504e-02 9.560e-01 2.233e-01 -0.202 0.840147
SmokerCurrentno -6.012e-01 5.481e-01 2.049e-01 -2.934 0.003343 **
SmokerCurrentyes NA NA 0.000e+00 NA NA
Med.Statin.LLDno 3.413e-01 1.407e+00 2.178e-01 1.567 0.117091
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 3.958e-01 1.486e+00 2.585e-01 1.531 0.125667
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD -1.933e-02 9.809e-01 4.975e-03 -3.885 0.000102 ***
BMI 5.855e-02 1.060e+00 2.624e-02 2.231 0.025676 *
CAD_history 1.486e-01 1.160e+00 2.035e-01 0.730 0.465433
Stroke_history 2.847e-02 1.029e+00 2.032e-01 0.140 0.888591
Peripheral.interv 6.295e-01 1.877e+00 2.185e-01 2.881 0.003959 **
stenose0-49% -1.552e+01 1.820e-07 2.775e+03 -0.006 0.995538
stenose50-70% -8.172e-01 4.417e-01 8.704e-01 -0.939 0.347787
stenose70-90% -2.432e-01 7.841e-01 7.292e-01 -0.333 0.738758
stenose90-99% -2.113e-01 8.095e-01 7.269e-01 -0.291 0.771249
stenose100% (Occlusion) 1.271e-01 1.136e+00 1.243e+00 0.102 0.918513
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% -1.545e+01 1.947e-07 4.214e+03 -0.004 0.997074
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
IL6_pg_ug_2015_rank[ 0.00109,3.33061] 4.243e-02 1.043e+00 1.977e-01 0.215 0.830048
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00104,3.34148] 1.177e+00 8.495e-01 0.79708 1.7386
Age 1.038e+00 9.630e-01 1.01235 1.0652
Gendermale 1.763e+00 5.671e-01 1.12091 2.7737
Hypertension.compositeno 5.849e-01 1.710e+00 0.27877 1.2271
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 9.560e-01 1.046e+00 0.61712 1.4808
SmokerCurrentno 5.481e-01 1.824e+00 0.36684 0.8190
SmokerCurrentyes NA NA NA NA
Med.Statin.LLDno 1.407e+00 7.109e-01 0.91800 2.1557
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 1.486e+00 6.731e-01 0.89515 2.4654
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 9.809e-01 1.020e+00 0.97134 0.9905
BMI 1.060e+00 9.431e-01 1.00714 1.1163
CAD_history 1.160e+00 8.620e-01 0.77854 1.7288
Stroke_history 1.029e+00 9.719e-01 0.69081 1.5324
Peripheral.interv 1.877e+00 5.329e-01 1.22298 2.8795
stenose0-49% 1.820e-07 5.496e+06 0.00000 Inf
stenose50-70% 4.417e-01 2.264e+00 0.08022 2.4320
stenose70-90% 7.841e-01 1.275e+00 0.18778 3.2742
stenose90-99% 8.095e-01 1.235e+00 0.19476 3.3647
stenose100% (Occlusion) 1.136e+00 8.806e-01 0.09940 12.9735
stenoseNA NA NA NA NA
stenose50-99% 1.947e-07 5.136e+06 0.00000 Inf
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
IL6_pg_ug_2015_rank[ 0.00109,3.33061] 1.043e+00 9.585e-01 0.70818 1.5371
Concordance= 0.707 (se = 0.023 )
Likelihood ratio test= 69.11 on 20 df, p=3e-07
Wald test = 39.27 on 20 df, p=0.006
Score (logrank) test = 67.72 on 20 df, p=4e-07
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_pg_ug_2015_rank ' and its association to ' epmajor.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epmajor.3years
Protein...................: MCP1_pg_ug_2015_rank
Effect size...............: 0.163144
Standard error............: 0.198954
Odds ratio (effect size)..: 1.177
Lower 95% CI..............: 0.797
Upper 95% CI..............: 1.739
T-value...................: 0.820007
P-value...................: 0.4122119
Sample size in model......: 1001
Number of events..........: 114
* Analyzing the effect of plaque proteins on [epstroke.3years].
- creating temporary SE for this work.
- making a 'Surv' object and adding this to temporary dataframe.
- making strata of each of the plaque proteins and start survival analysis.
> processing [IL6_rank]; 1 out of 5 proteins.
> cross tabulation of IL6_rank-stratum.
[-1.48329,0.00474) [ 0.00474,3.10694]
265 264
> fitting the model for IL6_rank-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + IL6_pg_ug_2015_rank, data = TEMP.DF)
n= 368, number of events= 26
(2020 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00474,3.10694] -1.558e-01 8.557e-01 4.112e-01 -0.379 0.705
Age 4.907e-02 1.050e+00 2.821e-02 1.739 0.082 .
Gendermale 6.220e-01 1.863e+00 5.170e-01 1.203 0.229
Hypertension.compositeno -4.395e-01 6.444e-01 7.733e-01 -0.568 0.570
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes 2.188e-01 1.245e+00 4.921e-01 0.445 0.657
SmokerCurrentno -3.138e-01 7.307e-01 4.315e-01 -0.727 0.467
SmokerCurrentyes NA NA 0.000e+00 NA NA
Med.Statin.LLDno 2.360e-01 1.266e+00 4.439e-01 0.532 0.595
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 3.981e-01 1.489e+00 6.140e-01 0.648 0.517
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD -1.981e-03 9.980e-01 1.108e-02 -0.179 0.858
BMI 2.002e-02 1.020e+00 5.918e-02 0.338 0.735
CAD_history 1.966e-01 1.217e+00 4.458e-01 0.441 0.659
Stroke_history 3.242e-01 1.383e+00 4.081e-01 0.794 0.427
Peripheral.interv -5.563e-01 5.733e-01 6.377e-01 -0.872 0.383
stenose0-49% -1.864e+01 8.070e-09 1.496e+04 -0.001 0.999
stenose50-70% -1.832e+01 1.105e-08 5.344e+03 -0.003 0.997
stenose70-90% -1.118e+00 3.269e-01 1.200e+00 -0.932 0.352
stenose90-99% -1.281e+00 2.779e-01 1.218e+00 -1.051 0.293
stenose100% (Occlusion) NA NA 0.000e+00 NA NA
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% NA NA 0.000e+00 NA NA
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
IL6_pg_ug_2015_rank 2.528e-02 1.026e+00 2.000e-01 0.126 0.899
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00474,3.10694] 8.557e-01 1.169e+00 0.38224 1.916
Age 1.050e+00 9.521e-01 0.99379 1.110
Gendermale 1.863e+00 5.369e-01 0.67615 5.131
Hypertension.compositeno 6.444e-01 1.552e+00 0.14155 2.933
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 1.245e+00 8.035e-01 0.47437 3.265
SmokerCurrentno 7.307e-01 1.369e+00 0.31362 1.702
SmokerCurrentyes NA NA NA NA
Med.Statin.LLDno 1.266e+00 7.897e-01 0.53048 3.022
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 1.489e+00 6.716e-01 0.44694 4.961
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 9.980e-01 1.002e+00 0.97658 1.020
BMI 1.020e+00 9.802e-01 0.90848 1.146
CAD_history 1.217e+00 8.215e-01 0.50803 2.917
Stroke_history 1.383e+00 7.231e-01 0.62143 3.077
Peripheral.interv 5.733e-01 1.744e+00 0.16427 2.001
stenose0-49% 8.070e-09 1.239e+08 0.00000 Inf
stenose50-70% 1.105e-08 9.053e+07 0.00000 Inf
stenose70-90% 3.269e-01 3.059e+00 0.03111 3.436
stenose90-99% 2.779e-01 3.599e+00 0.02551 3.026
stenose100% (Occlusion) NA NA NA NA
stenoseNA NA NA NA NA
stenose50-99% NA NA NA NA
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
IL6_pg_ug_2015_rank 1.026e+00 9.750e-01 0.69298 1.518
Concordance= 0.697 (se = 0.05 )
Likelihood ratio test= 13.52 on 18 df, p=0.8
Wald test = 10.22 on 18 df, p=0.9
Score (logrank) test = 12.74 on 18 df, p=0.8
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' IL6_rank ' and its association to ' epstroke.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epstroke.3years
Protein...................: IL6_rank
Effect size...............: -0.1558
Standard error............: 0.411186
Odds ratio (effect size)..: 0.856
Lower 95% CI..............: 0.382
Upper 95% CI..............: 1.916
T-value...................: -0.378903
P-value...................: 0.7047602
Sample size in model......: 368
Number of events..........: 26
> processing [MCP1_rank]; 2 out of 5 proteins.
> cross tabulation of MCP1_rank-stratum.
[-2.41053,0.00444) [ 0.00444,3.12635]
283 282
> fitting the model for MCP1_rank-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + IL6_pg_ug_2015_rank, data = TEMP.DF)
n= 392, number of events= 25
(1996 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00444,3.12635] -5.328e-01 5.870e-01 4.316e-01 -1.234 0.2171
Age 5.067e-02 1.052e+00 2.851e-02 1.777 0.0755 .
Gendermale 6.017e-01 1.825e+00 5.221e-01 1.152 0.2492
Hypertension.compositeno -4.954e-01 6.093e-01 7.676e-01 -0.645 0.5186
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes -6.285e-02 9.391e-01 5.253e-01 -0.120 0.9048
SmokerCurrentno -2.323e-01 7.927e-01 4.489e-01 -0.518 0.6047
SmokerCurrentyes NA NA 0.000e+00 NA NA
Med.Statin.LLDno 3.932e-01 1.482e+00 4.510e-01 0.872 0.3834
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 2.429e-01 1.275e+00 6.182e-01 0.393 0.6944
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD -1.360e-03 9.986e-01 1.164e-02 -0.117 0.9069
BMI 1.719e-02 1.017e+00 5.977e-02 0.288 0.7737
CAD_history 1.039e-01 1.110e+00 4.651e-01 0.223 0.8232
Stroke_history 3.472e-01 1.415e+00 4.174e-01 0.832 0.4055
Peripheral.interv -4.019e-01 6.690e-01 6.389e-01 -0.629 0.5293
stenose0-49% -1.875e+01 7.162e-09 1.687e+04 -0.001 0.9991
stenose50-70% -1.832e+01 1.109e-08 5.743e+03 -0.003 0.9975
stenose70-90% -1.081e+00 3.392e-01 1.194e+00 -0.905 0.3653
stenose90-99% -1.274e+00 2.797e-01 1.221e+00 -1.044 0.2966
stenose100% (Occlusion) NA NA 0.000e+00 NA NA
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% NA NA 0.000e+00 NA NA
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
IL6_pg_ug_2015_rank 8.011e-02 1.083e+00 2.080e-01 0.385 0.7001
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00444,3.12635] 5.870e-01 1.704e+00 0.25192 1.368
Age 1.052e+00 9.506e-01 0.99481 1.112
Gendermale 1.825e+00 5.479e-01 0.65593 5.079
Hypertension.compositeno 6.093e-01 1.641e+00 0.13536 2.743
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 9.391e-01 1.065e+00 0.33541 2.629
SmokerCurrentno 7.927e-01 1.262e+00 0.32887 1.911
SmokerCurrentyes NA NA NA NA
Med.Statin.LLDno 1.482e+00 6.749e-01 0.61212 3.586
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 1.275e+00 7.844e-01 0.37954 4.282
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 9.986e-01 1.001e+00 0.97612 1.022
BMI 1.017e+00 9.830e-01 0.90488 1.144
CAD_history 1.110e+00 9.013e-01 0.44588 2.761
Stroke_history 1.415e+00 7.067e-01 0.62443 3.207
Peripheral.interv 6.690e-01 1.495e+00 0.19124 2.340
stenose0-49% 7.162e-09 1.396e+08 0.00000 Inf
stenose50-70% 1.109e-08 9.020e+07 0.00000 Inf
stenose70-90% 3.392e-01 2.949e+00 0.03264 3.524
stenose90-99% 2.797e-01 3.575e+00 0.02557 3.059
stenose100% (Occlusion) NA NA NA NA
stenoseNA NA NA NA NA
stenose50-99% NA NA NA NA
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
IL6_pg_ug_2015_rank 1.083e+00 9.230e-01 0.72074 1.629
Concordance= 0.697 (se = 0.05 )
Likelihood ratio test= 14.65 on 18 df, p=0.7
Wald test = 12.01 on 18 df, p=0.8
Score (logrank) test = 14.49 on 18 df, p=0.7
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_rank ' and its association to ' epstroke.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epstroke.3years
Protein...................: MCP1_rank
Effect size...............: -0.532752
Standard error............: 0.431585
Odds ratio (effect size)..: 0.587
Lower 95% CI..............: 0.252
Upper 95% CI..............: 1.368
T-value...................: -1.234407
P-value...................: 0.2170514
Sample size in model......: 392
Number of events..........: 25
> processing [IL6_pg_ug_2015_rank]; 3 out of 5 proteins.
> cross tabulation of IL6_pg_ug_2015_rank-stratum.
[-3.33061,0.00109) [ 0.00109,3.33061]
577 577
> fitting the model for IL6_pg_ug_2015_rank-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + IL6_pg_ug_2015_rank, data = TEMP.DF)
n= 1002, number of events= 58
(1386 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00109,3.33061] -1.438e-01 8.660e-01 2.656e-01 -0.541 0.58818
Age 5.026e-02 1.052e+00 1.793e-02 2.803 0.00506 **
Gendermale 2.919e-01 1.339e+00 3.000e-01 0.973 0.33043
Hypertension.compositeno -2.080e-01 8.122e-01 4.442e-01 -0.468 0.63956
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes -6.073e-02 9.411e-01 3.197e-01 -0.190 0.84935
SmokerCurrentno -3.427e-01 7.098e-01 2.939e-01 -1.166 0.24358
SmokerCurrentyes NA NA 0.000e+00 NA NA
Med.Statin.LLDno 3.978e-01 1.488e+00 2.912e-01 1.366 0.17192
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 3.468e-01 1.414e+00 3.748e-01 0.925 0.35485
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD -5.639e-03 9.944e-01 7.004e-03 -0.805 0.42078
BMI 9.236e-02 1.097e+00 3.357e-02 2.751 0.00593 **
CAD_history -5.750e-01 5.627e-01 3.288e-01 -1.749 0.08031 .
Stroke_history 3.241e-01 1.383e+00 2.748e-01 1.179 0.23826
Peripheral.interv 4.887e-01 1.630e+00 3.276e-01 1.492 0.13578
stenose0-49% -1.559e+01 1.699e-07 3.740e+03 -0.004 0.99667
stenose50-70% -5.898e-01 5.544e-01 1.159e+00 -0.509 0.61097
stenose70-90% -3.521e-01 7.032e-01 1.025e+00 -0.343 0.73127
stenose90-99% -3.578e-01 6.992e-01 1.024e+00 -0.349 0.72672
stenose100% (Occlusion) 3.875e-01 1.473e+00 1.439e+00 0.269 0.78767
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% -1.537e+01 2.109e-07 5.615e+03 -0.003 0.99782
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
IL6_pg_ug_2015_rank[ 0.00109,3.33061] NA NA 0.000e+00 NA NA
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00109,3.33061] 8.660e-01 1.155e+00 0.51454 1.458
Age 1.052e+00 9.510e-01 1.01523 1.089
Gendermale 1.339e+00 7.468e-01 0.74380 2.411
Hypertension.compositeno 8.122e-01 1.231e+00 0.34009 1.940
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 9.411e-01 1.063e+00 0.50289 1.761
SmokerCurrentno 7.098e-01 1.409e+00 0.39898 1.263
SmokerCurrentyes NA NA NA NA
Med.Statin.LLDno 1.488e+00 6.718e-01 0.84120 2.634
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 1.414e+00 7.070e-01 0.67854 2.949
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 9.944e-01 1.006e+00 0.98082 1.008
BMI 1.097e+00 9.118e-01 1.02692 1.171
CAD_history 5.627e-01 1.777e+00 0.29542 1.072
Stroke_history 1.383e+00 7.232e-01 0.80691 2.370
Peripheral.interv 1.630e+00 6.134e-01 0.85779 3.098
stenose0-49% 1.699e-07 5.887e+06 0.00000 Inf
stenose50-70% 5.544e-01 1.804e+00 0.05713 5.380
stenose70-90% 7.032e-01 1.422e+00 0.09429 5.245
stenose90-99% 6.992e-01 1.430e+00 0.09401 5.200
stenose100% (Occlusion) 1.473e+00 6.788e-01 0.08784 24.711
stenoseNA NA NA NA NA
stenose50-99% 2.109e-07 4.742e+06 0.00000 Inf
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
IL6_pg_ug_2015_rank[ 0.00109,3.33061] NA NA NA NA
Concordance= 0.682 (se = 0.036 )
Likelihood ratio test= 27.19 on 19 df, p=0.1
Wald test = 22.76 on 19 df, p=0.2
Score (logrank) test = 26.28 on 19 df, p=0.1
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' IL6_pg_ug_2015_rank ' and its association to ' epstroke.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epstroke.3years
Protein...................: IL6_pg_ug_2015_rank
Effect size...............: -0.143835
Standard error............: 0.265636
Odds ratio (effect size)..: 0.866
Lower 95% CI..............: 0.515
Upper 95% CI..............: 1.458
T-value...................: -0.541475
P-value...................: 0.5881802
Sample size in model......: 1002
Number of events..........: 58
> processing [IL6R_pg_ug_2015_rank]; 4 out of 5 proteins.
> cross tabulation of IL6R_pg_ug_2015_rank-stratum.
[-3.33109,0.00108) [ 0.00108,3.33109]
578 578
> fitting the model for IL6R_pg_ug_2015_rank-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + IL6_pg_ug_2015_rank, data = TEMP.DF)
n= 970, number of events= 57
(1418 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00108,3.33109] 1.922e-01 1.212e+00 2.849e-01 0.675 0.49989
Age 4.981e-02 1.051e+00 1.823e-02 2.733 0.00628 **
Gendermale 2.945e-01 1.342e+00 3.063e-01 0.961 0.33642
Hypertension.compositeno -2.189e-01 8.034e-01 4.448e-01 -0.492 0.62257
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes -1.150e-01 8.914e-01 3.264e-01 -0.352 0.72473
SmokerCurrentno -3.442e-01 7.088e-01 2.961e-01 -1.162 0.24510
SmokerCurrentyes NA NA 0.000e+00 NA NA
Med.Statin.LLDno 3.949e-01 1.484e+00 2.960e-01 1.334 0.18224
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 3.615e-01 1.436e+00 3.750e-01 0.964 0.33495
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD -3.047e-03 9.970e-01 7.227e-03 -0.422 0.67331
BMI 9.901e-02 1.104e+00 3.632e-02 2.726 0.00640 **
CAD_history -5.360e-01 5.851e-01 3.312e-01 -1.618 0.10561
Stroke_history 3.457e-01 1.413e+00 2.759e-01 1.253 0.21027
Peripheral.interv 5.297e-01 1.698e+00 3.294e-01 1.608 0.10779
stenose0-49% -1.417e+01 7.014e-07 2.855e+03 -0.005 0.99604
stenose50-70% -5.990e-01 5.494e-01 1.159e+00 -0.517 0.60543
stenose70-90% -3.751e-01 6.872e-01 1.026e+00 -0.366 0.71466
stenose90-99% -4.397e-01 6.443e-01 1.027e+00 -0.428 0.66855
stenose100% (Occlusion) 3.483e-01 1.417e+00 1.436e+00 0.242 0.80840
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% -1.446e+01 5.246e-07 3.454e+03 -0.004 0.99666
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
IL6_pg_ug_2015_rank[ 0.00109,3.33061] -1.892e-01 8.277e-01 2.775e-01 -0.682 0.49539
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00108,3.33109] 1.212e+00 8.251e-01 0.69340 2.118
Age 1.051e+00 9.514e-01 1.01418 1.089
Gendermale 1.342e+00 7.449e-01 0.73643 2.447
Hypertension.compositeno 8.034e-01 1.245e+00 0.33600 1.921
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 8.914e-01 1.122e+00 0.47012 1.690
SmokerCurrentno 7.088e-01 1.411e+00 0.39671 1.266
SmokerCurrentyes NA NA NA NA
Med.Statin.LLDno 1.484e+00 6.738e-01 0.83083 2.651
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 1.436e+00 6.966e-01 0.68841 2.993
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 9.970e-01 1.003e+00 0.98294 1.011
BMI 1.104e+00 9.057e-01 1.02822 1.186
CAD_history 5.851e-01 1.709e+00 0.30571 1.120
Stroke_history 1.413e+00 7.078e-01 0.82275 2.426
Peripheral.interv 1.698e+00 5.888e-01 0.89060 3.239
stenose0-49% 7.014e-07 1.426e+06 0.00000 Inf
stenose50-70% 5.494e-01 1.820e+00 0.05661 5.331
stenose70-90% 6.872e-01 1.455e+00 0.09199 5.134
stenose90-99% 6.443e-01 1.552e+00 0.08609 4.821
stenose100% (Occlusion) 1.417e+00 7.059e-01 0.08484 23.655
stenoseNA NA NA NA NA
stenose50-99% 5.246e-07 1.906e+06 0.00000 Inf
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
IL6_pg_ug_2015_rank[ 0.00109,3.33061] 8.277e-01 1.208e+00 0.48049 1.426
Concordance= 0.682 (se = 0.037 )
Likelihood ratio test= 26.8 on 20 df, p=0.1
Wald test = 24.83 on 20 df, p=0.2
Score (logrank) test = 26.17 on 20 df, p=0.2
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' IL6R_pg_ug_2015_rank ' and its association to ' epstroke.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epstroke.3years
Protein...................: IL6R_pg_ug_2015_rank
Effect size...............: 0.192191
Standard error............: 0.284869
Odds ratio (effect size)..: 1.212
Lower 95% CI..............: 0.693
Upper 95% CI..............: 2.118
T-value...................: 0.674663
P-value...................: 0.49989
Sample size in model......: 970
Number of events..........: 57
> processing [MCP1_pg_ug_2015_rank]; 5 out of 5 proteins.
> cross tabulation of MCP1_pg_ug_2015_rank-stratum.
[-3.34148,0.00104) [ 0.00104,3.34148]
600 600
> fitting the model for MCP1_pg_ug_2015_rank-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + IL6_pg_ug_2015_rank, data = TEMP.DF)
n= 1001, number of events= 58
(1387 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00104,3.34148] 9.854e-02 1.104e+00 2.811e-01 0.351 0.72590
Age 5.007e-02 1.051e+00 1.795e-02 2.789 0.00529 **
Gendermale 2.863e-01 1.331e+00 3.004e-01 0.953 0.34056
Hypertension.compositeno -2.175e-01 8.045e-01 4.449e-01 -0.489 0.62490
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes -5.951e-02 9.422e-01 3.197e-01 -0.186 0.85233
SmokerCurrentno -3.434e-01 7.093e-01 2.943e-01 -1.167 0.24327
SmokerCurrentyes NA NA 0.000e+00 NA NA
Med.Statin.LLDno 3.975e-01 1.488e+00 2.915e-01 1.364 0.17269
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 3.438e-01 1.410e+00 3.744e-01 0.918 0.35853
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD -5.790e-03 9.942e-01 7.008e-03 -0.826 0.40874
BMI 9.346e-02 1.098e+00 3.390e-02 2.757 0.00583 **
CAD_history -5.704e-01 5.653e-01 3.291e-01 -1.734 0.08301 .
Stroke_history 3.137e-01 1.368e+00 2.761e-01 1.136 0.25587
Peripheral.interv 4.846e-01 1.624e+00 3.278e-01 1.478 0.13932
stenose0-49% -1.553e+01 1.806e-07 3.750e+03 -0.004 0.99670
stenose50-70% -5.725e-01 5.641e-01 1.161e+00 -0.493 0.62181
stenose70-90% -3.214e-01 7.252e-01 1.029e+00 -0.312 0.75480
stenose90-99% -3.196e-01 7.265e-01 1.029e+00 -0.311 0.75615
stenose100% (Occlusion) 4.363e-01 1.547e+00 1.445e+00 0.302 0.76266
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% -1.540e+01 2.048e-07 5.618e+03 -0.003 0.99781
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
IL6_pg_ug_2015_rank[ 0.00109,3.33061] -1.713e-01 8.426e-01 2.758e-01 -0.621 0.53451
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00104,3.34148] 1.104e+00 9.062e-01 0.63612 1.914
Age 1.051e+00 9.512e-01 1.01499 1.089
Gendermale 1.331e+00 7.511e-01 0.73900 2.399
Hypertension.compositeno 8.045e-01 1.243e+00 0.33641 1.924
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 9.422e-01 1.061e+00 0.50353 1.763
SmokerCurrentno 7.093e-01 1.410e+00 0.39840 1.263
SmokerCurrentyes NA NA NA NA
Med.Statin.LLDno 1.488e+00 6.720e-01 0.84044 2.635
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 1.410e+00 7.091e-01 0.67701 2.938
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 9.942e-01 1.006e+00 0.98066 1.008
BMI 1.098e+00 9.108e-01 1.02739 1.173
CAD_history 5.653e-01 1.769e+00 0.29660 1.077
Stroke_history 1.368e+00 7.307e-01 0.79658 2.351
Peripheral.interv 1.624e+00 6.159e-01 0.85395 3.087
stenose0-49% 1.806e-07 5.537e+06 0.00000 Inf
stenose50-70% 5.641e-01 1.773e+00 0.05800 5.486
stenose70-90% 7.252e-01 1.379e+00 0.09652 5.449
stenose90-99% 7.265e-01 1.377e+00 0.09666 5.460
stenose100% (Occlusion) 1.547e+00 6.464e-01 0.09112 26.266
stenoseNA NA NA NA NA
stenose50-99% 2.048e-07 4.883e+06 0.00000 Inf
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
IL6_pg_ug_2015_rank[ 0.00109,3.33061] 8.426e-01 1.187e+00 0.49076 1.447
Concordance= 0.684 (se = 0.036 )
Likelihood ratio test= 27.27 on 20 df, p=0.1
Wald test = 22.73 on 20 df, p=0.3
Score (logrank) test = 26.34 on 20 df, p=0.2
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_pg_ug_2015_rank ' and its association to ' epstroke.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epstroke.3years
Protein...................: MCP1_pg_ug_2015_rank
Effect size...............: 0.098544
Standard error............: 0.28108
Odds ratio (effect size)..: 1.104
Lower 95% CI..............: 0.636
Upper 95% CI..............: 1.915
T-value...................: 0.350591
P-value...................: 0.7258952
Sample size in model......: 1001
Number of events..........: 58
* Analyzing the effect of plaque proteins on [epcoronary.3years].
- creating temporary SE for this work.
- making a 'Surv' object and adding this to temporary dataframe.
- making strata of each of the plaque proteins and start survival analysis.
> processing [IL6_rank]; 1 out of 5 proteins.
> cross tabulation of IL6_rank-stratum.
[-1.48329,0.00474) [ 0.00474,3.10694]
265 264
> fitting the model for IL6_rank-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + IL6_pg_ug_2015_rank, data = TEMP.DF)
n= 368, number of events= 35
(2020 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00474,3.10694] 3.265e-02 1.033e+00 3.620e-01 0.090 0.9281
Age 4.434e-02 1.045e+00 2.524e-02 1.757 0.0790 .
Gendermale 8.463e-01 2.331e+00 4.995e-01 1.694 0.0902 .
Hypertension.compositeno -6.475e-01 5.233e-01 7.502e-01 -0.863 0.3881
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes 1.182e-01 1.125e+00 4.279e-01 0.276 0.7823
SmokerCurrentno -3.267e-01 7.213e-01 3.670e-01 -0.890 0.3734
SmokerCurrentyes NA NA 0.000e+00 NA NA
Med.Statin.LLDno 1.030e-01 1.109e+00 4.070e-01 0.253 0.8002
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 4.438e-01 1.559e+00 4.804e-01 0.924 0.3556
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD -1.345e-02 9.866e-01 9.552e-03 -1.408 0.1591
BMI -2.755e-02 9.728e-01 5.681e-02 -0.485 0.6277
CAD_history 7.157e-01 2.046e+00 3.731e-01 1.918 0.0551 .
Stroke_history -5.258e-01 5.911e-01 4.121e-01 -1.276 0.2020
Peripheral.interv 2.659e-01 1.305e+00 4.098e-01 0.649 0.5164
stenose0-49% -4.150e-01 6.604e-01 9.455e+03 0.000 1.0000
stenose50-70% 1.616e+01 1.039e+07 5.837e+03 0.003 0.9978
stenose70-90% 1.617e+01 1.056e+07 5.837e+03 0.003 0.9978
stenose90-99% 1.616e+01 1.046e+07 5.837e+03 0.003 0.9978
stenose100% (Occlusion) NA NA 0.000e+00 NA NA
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% NA NA 0.000e+00 NA NA
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
IL6_pg_ug_2015_rank 1.736e-01 1.190e+00 1.820e-01 0.954 0.3400
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00474,3.10694] 1.033e+00 9.679e-01 0.5082 2.100
Age 1.045e+00 9.566e-01 0.9949 1.098
Gendermale 2.331e+00 4.290e-01 0.8758 6.204
Hypertension.compositeno 5.233e-01 1.911e+00 0.1203 2.277
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 1.125e+00 8.885e-01 0.4865 2.604
SmokerCurrentno 7.213e-01 1.386e+00 0.3513 1.481
SmokerCurrentyes NA NA NA NA
Med.Statin.LLDno 1.109e+00 9.021e-01 0.4992 2.461
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 1.559e+00 6.416e-01 0.6079 3.996
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 9.866e-01 1.014e+00 0.9683 1.005
BMI 9.728e-01 1.028e+00 0.8703 1.087
CAD_history 2.046e+00 4.888e-01 0.9846 4.250
Stroke_history 5.911e-01 1.692e+00 0.2636 1.326
Peripheral.interv 1.305e+00 7.665e-01 0.5844 2.913
stenose0-49% 6.604e-01 1.514e+00 0.0000 Inf
stenose50-70% 1.039e+07 9.628e-08 0.0000 Inf
stenose70-90% 1.056e+07 9.465e-08 0.0000 Inf
stenose90-99% 1.046e+07 9.562e-08 0.0000 Inf
stenose100% (Occlusion) NA NA NA NA
stenoseNA NA NA NA NA
stenose50-99% NA NA NA NA
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
IL6_pg_ug_2015_rank 1.190e+00 8.406e-01 0.8327 1.699
Concordance= 0.769 (se = 0.037 )
Likelihood ratio test= 29.04 on 18 df, p=0.05
Wald test = 19.05 on 18 df, p=0.4
Score (logrank) test = 30.5 on 18 df, p=0.03
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' IL6_rank ' and its association to ' epcoronary.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epcoronary.3years
Protein...................: IL6_rank
Effect size...............: 0.032645
Standard error............: 0.361965
Odds ratio (effect size)..: 1.033
Lower 95% CI..............: 0.508
Upper 95% CI..............: 2.1
T-value...................: 0.09019
P-value...................: 0.9281365
Sample size in model......: 368
Number of events..........: 35
> processing [MCP1_rank]; 2 out of 5 proteins.
> cross tabulation of MCP1_rank-stratum.
[-2.41053,0.00444) [ 0.00444,3.12635]
283 282
> fitting the model for MCP1_rank-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + IL6_pg_ug_2015_rank, data = TEMP.DF)
n= 392, number of events= 36
(1996 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00444,3.12635] -1.373e-02 9.864e-01 3.585e-01 -0.038 0.9694
Age 3.619e-02 1.037e+00 2.487e-02 1.455 0.1455
Gendermale 6.864e-01 1.987e+00 4.617e-01 1.487 0.1371
Hypertension.compositeno -2.809e-01 7.551e-01 6.331e-01 -0.444 0.6572
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes 2.804e-01 1.324e+00 3.981e-01 0.704 0.4812
SmokerCurrentno -3.924e-01 6.754e-01 3.591e-01 -1.093 0.2745
SmokerCurrentyes NA NA 0.000e+00 NA NA
Med.Statin.LLDno 1.115e-01 1.118e+00 4.062e-01 0.274 0.7837
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 4.027e-01 1.496e+00 4.781e-01 0.842 0.3995
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD -1.685e-02 9.833e-01 9.425e-03 -1.788 0.0738 .
BMI -1.446e-02 9.856e-01 5.293e-02 -0.273 0.7848
CAD_history 7.367e-01 2.089e+00 3.648e-01 2.020 0.0434 *
Stroke_history -3.350e-01 7.153e-01 3.837e-01 -0.873 0.3826
Peripheral.interv 3.051e-01 1.357e+00 4.015e-01 0.760 0.4474
stenose0-49% -4.331e-01 6.485e-01 9.334e+03 0.000 1.0000
stenose50-70% 1.616e+01 1.042e+07 5.888e+03 0.003 0.9978
stenose70-90% 1.607e+01 9.491e+06 5.888e+03 0.003 0.9978
stenose90-99% 1.612e+01 1.004e+07 5.888e+03 0.003 0.9978
stenose100% (Occlusion) NA NA 0.000e+00 NA NA
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% NA NA 0.000e+00 NA NA
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
IL6_pg_ug_2015_rank 2.116e-01 1.236e+00 1.763e-01 1.200 0.2301
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00444,3.12635] 9.864e-01 1.014e+00 0.4886 1.991
Age 1.037e+00 9.645e-01 0.9875 1.089
Gendermale 1.987e+00 5.034e-01 0.8037 4.911
Hypertension.compositeno 7.551e-01 1.324e+00 0.2183 2.612
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 1.324e+00 7.555e-01 0.6066 2.888
SmokerCurrentno 6.754e-01 1.481e+00 0.3341 1.365
SmokerCurrentyes NA NA NA NA
Med.Statin.LLDno 1.118e+00 8.945e-01 0.5043 2.478
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 1.496e+00 6.685e-01 0.5861 3.818
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 9.833e-01 1.017e+00 0.9653 1.002
BMI 9.856e-01 1.015e+00 0.8885 1.093
CAD_history 2.089e+00 4.787e-01 1.0220 4.270
Stroke_history 7.153e-01 1.398e+00 0.3372 1.517
Peripheral.interv 1.357e+00 7.371e-01 0.6176 2.980
stenose0-49% 6.485e-01 1.542e+00 0.0000 Inf
stenose50-70% 1.042e+07 9.596e-08 0.0000 Inf
stenose70-90% 9.491e+06 1.054e-07 0.0000 Inf
stenose90-99% 1.004e+07 9.964e-08 0.0000 Inf
stenose100% (Occlusion) NA NA NA NA
stenoseNA NA NA NA NA
stenose50-99% NA NA NA NA
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
IL6_pg_ug_2015_rank 1.236e+00 8.093e-01 0.8747 1.745
Concordance= 0.749 (se = 0.041 )
Likelihood ratio test= 27.64 on 18 df, p=0.07
Wald test = 16.45 on 18 df, p=0.6
Score (logrank) test = 28.99 on 18 df, p=0.05
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_rank ' and its association to ' epcoronary.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epcoronary.3years
Protein...................: MCP1_rank
Effect size...............: -0.013729
Standard error............: 0.358462
Odds ratio (effect size)..: 0.986
Lower 95% CI..............: 0.489
Upper 95% CI..............: 1.991
T-value...................: -0.038301
P-value...................: 0.9694478
Sample size in model......: 392
Number of events..........: 36
> processing [IL6_pg_ug_2015_rank]; 3 out of 5 proteins.
> cross tabulation of IL6_pg_ug_2015_rank-stratum.
[-3.33061,0.00109) [ 0.00109,3.33061]
577 577
> fitting the model for IL6_pg_ug_2015_rank-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + IL6_pg_ug_2015_rank, data = TEMP.DF)
n= 1002, number of events= 79
(1386 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00109,3.33061] 5.813e-02 1.060e+00 2.304e-01 0.252 0.80083
Age -3.043e-03 9.970e-01 1.510e-02 -0.201 0.84032
Gendermale 7.141e-01 2.042e+00 3.011e-01 2.372 0.01771 *
Hypertension.compositeno -7.882e-01 4.546e-01 5.252e-01 -1.501 0.13341
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes -1.803e-01 8.351e-01 2.782e-01 -0.648 0.51711
SmokerCurrentno -5.750e-01 5.627e-01 2.428e-01 -2.369 0.01785 *
SmokerCurrentyes NA NA 0.000e+00 NA NA
Med.Statin.LLDno 2.493e-01 1.283e+00 2.741e-01 0.909 0.36312
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 1.737e-01 1.190e+00 3.354e-01 0.518 0.60448
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD -2.040e-02 9.798e-01 5.967e-03 -3.419 0.00063 ***
BMI 1.169e-02 1.012e+00 3.338e-02 0.350 0.72620
CAD_history 9.403e-01 2.561e+00 2.410e-01 3.901 9.56e-05 ***
Stroke_history -1.024e-01 9.027e-01 2.526e-01 -0.405 0.68527
Peripheral.interv 4.301e-01 1.537e+00 2.613e-01 1.646 0.09979 .
stenose0-49% -1.581e+01 1.357e-07 3.608e+03 -0.004 0.99650
stenose50-70% -1.055e+00 3.483e-01 1.233e+00 -0.856 0.39220
stenose70-90% -1.716e-02 9.830e-01 1.022e+00 -0.017 0.98661
stenose90-99% -1.699e-01 8.437e-01 1.022e+00 -0.166 0.86794
stenose100% (Occlusion) -1.551e+01 1.841e-07 3.150e+03 -0.005 0.99607
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% 1.291e+00 3.637e+00 1.459e+00 0.885 0.37628
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
IL6_pg_ug_2015_rank[ 0.00109,3.33061] NA NA 0.000e+00 NA NA
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00109,3.33061] 1.060e+00 9.435e-01 0.67470 1.6649
Age 9.970e-01 1.003e+00 0.96789 1.0269
Gendermale 2.042e+00 4.896e-01 1.13195 3.6847
Hypertension.compositeno 4.546e-01 2.200e+00 0.16241 1.2727
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 8.351e-01 1.198e+00 0.48403 1.4407
SmokerCurrentno 5.627e-01 1.777e+00 0.34966 0.9055
SmokerCurrentyes NA NA NA NA
Med.Statin.LLDno 1.283e+00 7.793e-01 0.74978 2.1959
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 1.190e+00 8.405e-01 0.61655 2.2958
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 9.798e-01 1.021e+00 0.96842 0.9913
BMI 1.012e+00 9.884e-01 0.94768 1.0802
CAD_history 2.561e+00 3.905e-01 1.59668 4.1070
Stroke_history 9.027e-01 1.108e+00 0.55019 1.4810
Peripheral.interv 1.537e+00 6.504e-01 0.92119 2.5661
stenose0-49% 1.357e-07 7.370e+06 0.00000 Inf
stenose50-70% 3.483e-01 2.871e+00 0.03108 3.9018
stenose70-90% 9.830e-01 1.017e+00 0.13256 7.2891
stenose90-99% 8.437e-01 1.185e+00 0.11388 6.2514
stenose100% (Occlusion) 1.841e-07 5.432e+06 0.00000 Inf
stenoseNA NA NA NA NA
stenose50-99% 3.637e+00 2.750e-01 0.20825 63.5179
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
IL6_pg_ug_2015_rank[ 0.00109,3.33061] NA NA NA NA
Concordance= 0.738 (se = 0.027 )
Likelihood ratio test= 61.46 on 19 df, p=2e-06
Wald test = 60.06 on 19 df, p=4e-06
Score (logrank) test = 64.83 on 19 df, p=7e-07
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' IL6_pg_ug_2015_rank ' and its association to ' epcoronary.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epcoronary.3years
Protein...................: IL6_pg_ug_2015_rank
Effect size...............: 0.058129
Standard error............: 0.230422
Odds ratio (effect size)..: 1.06
Lower 95% CI..............: 0.675
Upper 95% CI..............: 1.665
T-value...................: 0.252272
P-value...................: 0.8008308
Sample size in model......: 1002
Number of events..........: 79
> processing [IL6R_pg_ug_2015_rank]; 4 out of 5 proteins.
> cross tabulation of IL6R_pg_ug_2015_rank-stratum.
[-3.33109,0.00108) [ 0.00108,3.33109]
578 578
> fitting the model for IL6R_pg_ug_2015_rank-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + IL6_pg_ug_2015_rank, data = TEMP.DF)
n= 970, number of events= 79
(1418 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00108,3.33109] 2.941e-01 1.342e+00 2.501e-01 1.176 0.239562
Age -2.902e-03 9.971e-01 1.518e-02 -0.191 0.848414
Gendermale 6.691e-01 1.953e+00 3.010e-01 2.223 0.026216 *
Hypertension.compositeno -7.890e-01 4.543e-01 5.262e-01 -1.499 0.133764
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes -1.543e-01 8.570e-01 2.782e-01 -0.555 0.579160
SmokerCurrentno -5.523e-01 5.756e-01 2.444e-01 -2.259 0.023868 *
SmokerCurrentyes NA NA 0.000e+00 NA NA
Med.Statin.LLDno 1.992e-01 1.220e+00 2.773e-01 0.718 0.472584
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 1.835e-01 1.201e+00 3.363e-01 0.546 0.585317
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD -1.969e-02 9.805e-01 5.957e-03 -3.306 0.000947 ***
BMI 1.099e-02 1.011e+00 3.388e-02 0.324 0.745737
CAD_history 9.533e-01 2.594e+00 2.411e-01 3.954 7.69e-05 ***
Stroke_history -1.332e-01 8.753e-01 2.524e-01 -0.528 0.597671
Peripheral.interv 3.997e-01 1.491e+00 2.606e-01 1.533 0.125201
stenose0-49% -1.580e+01 1.379e-07 4.034e+03 -0.004 0.996875
stenose50-70% -1.057e+00 3.475e-01 1.232e+00 -0.858 0.390766
stenose70-90% -9.532e-02 9.091e-01 1.024e+00 -0.093 0.925855
stenose90-99% -2.567e-01 7.736e-01 1.025e+00 -0.250 0.802245
stenose100% (Occlusion) -1.556e+01 1.749e-07 2.984e+03 -0.005 0.995840
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% 1.071e+00 2.920e+00 1.470e+00 0.729 0.465935
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
IL6_pg_ug_2015_rank[ 0.00109,3.33061] -7.198e-02 9.305e-01 2.421e-01 -0.297 0.766252
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00108,3.33109] 1.342e+00 7.452e-01 0.82199 2.1907
Age 9.971e-01 1.003e+00 0.96787 1.0272
Gendermale 1.953e+00 5.122e-01 1.08239 3.5222
Hypertension.compositeno 4.543e-01 2.201e+00 0.16197 1.2742
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 8.570e-01 1.167e+00 0.49684 1.4784
SmokerCurrentno 5.756e-01 1.737e+00 0.35652 0.9295
SmokerCurrentyes NA NA NA NA
Med.Statin.LLDno 1.220e+00 8.194e-01 0.70873 2.1014
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 1.201e+00 8.324e-01 0.62153 2.3222
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 9.805e-01 1.020e+00 0.96912 0.9920
BMI 1.011e+00 9.891e-01 0.94609 1.0805
CAD_history 2.594e+00 3.855e-01 1.61726 4.1613
Stroke_history 8.753e-01 1.142e+00 0.53373 1.4354
Peripheral.interv 1.491e+00 6.706e-01 0.89475 2.4856
stenose0-49% 1.379e-07 7.253e+06 0.00000 Inf
stenose50-70% 3.475e-01 2.878e+00 0.03108 3.8845
stenose70-90% 9.091e-01 1.100e+00 0.12210 6.7683
stenose90-99% 7.736e-01 1.293e+00 0.10374 5.7687
stenose100% (Occlusion) 1.749e-07 5.719e+06 0.00000 Inf
stenoseNA NA NA NA NA
stenose50-99% 2.920e+00 3.425e-01 0.16384 52.0301
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
IL6_pg_ug_2015_rank[ 0.00109,3.33061] 9.305e-01 1.075e+00 0.57893 1.4957
Concordance= 0.736 (se = 0.028 )
Likelihood ratio test= 61.86 on 20 df, p=4e-06
Wald test = 61 on 20 df, p=5e-06
Score (logrank) test = 66.22 on 20 df, p=7e-07
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' IL6R_pg_ug_2015_rank ' and its association to ' epcoronary.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epcoronary.3years
Protein...................: IL6R_pg_ug_2015_rank
Effect size...............: 0.294099
Standard error............: 0.250066
Odds ratio (effect size)..: 1.342
Lower 95% CI..............: 0.822
Upper 95% CI..............: 2.191
T-value...................: 1.176083
P-value...................: 0.2395617
Sample size in model......: 970
Number of events..........: 79
> processing [MCP1_pg_ug_2015_rank]; 5 out of 5 proteins.
> cross tabulation of MCP1_pg_ug_2015_rank-stratum.
[-3.34148,0.00104) [ 0.00104,3.34148]
600 600
> fitting the model for MCP1_pg_ug_2015_rank-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + IL6_pg_ug_2015_rank, data = TEMP.DF)
n= 1001, number of events= 79
(1387 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00104,3.34148] 1.074e-02 1.011e+00 2.418e-01 0.044 0.964569
Age -3.637e-03 9.964e-01 1.513e-02 -0.240 0.809994
Gendermale 7.173e-01 2.049e+00 3.011e-01 2.382 0.017207 *
Hypertension.compositeno -7.921e-01 4.529e-01 5.256e-01 -1.507 0.131811
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes -1.848e-01 8.313e-01 2.784e-01 -0.664 0.506811
SmokerCurrentno -5.678e-01 5.668e-01 2.429e-01 -2.338 0.019394 *
SmokerCurrentyes NA NA 0.000e+00 NA NA
Med.Statin.LLDno 2.635e-01 1.302e+00 2.744e-01 0.960 0.336808
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 1.715e-01 1.187e+00 3.354e-01 0.512 0.609000
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD -2.055e-02 9.797e-01 5.981e-03 -3.436 0.000589 ***
BMI 1.214e-02 1.012e+00 3.335e-02 0.364 0.715939
CAD_history 9.475e-01 2.579e+00 2.413e-01 3.927 8.6e-05 ***
Stroke_history -1.057e-01 8.997e-01 2.527e-01 -0.418 0.675695
Peripheral.interv 4.261e-01 1.531e+00 2.613e-01 1.631 0.102925
stenose0-49% -1.580e+01 1.370e-07 3.598e+03 -0.004 0.996495
stenose50-70% -1.058e+00 3.471e-01 1.234e+00 -0.857 0.391189
stenose70-90% -1.794e-02 9.822e-01 1.025e+00 -0.017 0.986043
stenose90-99% -1.636e-01 8.490e-01 1.026e+00 -0.160 0.873227
stenose100% (Occlusion) -1.550e+01 1.848e-07 3.151e+03 -0.005 0.996074
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% 1.266e+00 3.545e+00 1.462e+00 0.865 0.386797
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
IL6_pg_ug_2015_rank[ 0.00109,3.33061] 4.822e-02 1.049e+00 2.419e-01 0.199 0.841964
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00104,3.34148] 1.011e+00 9.893e-01 0.62928 1.6236
Age 9.964e-01 1.004e+00 0.96726 1.0264
Gendermale 2.049e+00 4.881e-01 1.13560 3.6969
Hypertension.compositeno 4.529e-01 2.208e+00 0.16166 1.2688
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 8.313e-01 1.203e+00 0.48168 1.4346
SmokerCurrentno 5.668e-01 1.764e+00 0.35208 0.9123
SmokerCurrentyes NA NA NA NA
Med.Statin.LLDno 1.302e+00 7.683e-01 0.76016 2.2285
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 1.187e+00 8.424e-01 0.61524 2.2906
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 9.797e-01 1.021e+00 0.96824 0.9912
BMI 1.012e+00 9.879e-01 0.94816 1.0806
CAD_history 2.579e+00 3.877e-01 1.60738 4.1388
Stroke_history 8.997e-01 1.112e+00 0.54825 1.4764
Peripheral.interv 1.531e+00 6.531e-01 0.91761 2.5552
stenose0-49% 1.370e-07 7.301e+06 0.00000 Inf
stenose50-70% 3.471e-01 2.881e+00 0.03091 3.8982
stenose70-90% 9.822e-01 1.018e+00 0.13167 7.3272
stenose90-99% 8.490e-01 1.178e+00 0.11375 6.3373
stenose100% (Occlusion) 1.848e-07 5.412e+06 0.00000 Inf
stenoseNA NA NA NA NA
stenose50-99% 3.545e+00 2.821e-01 0.20178 62.2819
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
IL6_pg_ug_2015_rank[ 0.00109,3.33061] 1.049e+00 9.529e-01 0.65322 1.6859
Concordance= 0.739 (se = 0.027 )
Likelihood ratio test= 61.78 on 20 df, p=4e-06
Wald test = 60.46 on 20 df, p=6e-06
Score (logrank) test = 65.18 on 20 df, p=1e-06
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_pg_ug_2015_rank ' and its association to ' epcoronary.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epcoronary.3years
Protein...................: MCP1_pg_ug_2015_rank
Effect size...............: 0.010741
Standard error............: 0.241803
Odds ratio (effect size)..: 1.011
Lower 95% CI..............: 0.629
Upper 95% CI..............: 1.624
T-value...................: 0.044421
P-value...................: 0.9645686
Sample size in model......: 1001
Number of events..........: 79
* Analyzing the effect of plaque proteins on [epcvdeath.3years].
- creating temporary SE for this work.
- making a 'Surv' object and adding this to temporary dataframe.
- making strata of each of the plaque proteins and start survival analysis.
> processing [IL6_rank]; 1 out of 5 proteins.
> cross tabulation of IL6_rank-stratum.
[-1.48329,0.00474) [ 0.00474,3.10694]
265 264
> fitting the model for IL6_rank-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + IL6_pg_ug_2015_rank, data = TEMP.DF)
n= 368, number of events= 16
(2020 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00474,3.10694] -5.348e-01 5.858e-01 5.697e-01 -0.939 0.34787
Age 1.204e-01 1.128e+00 4.428e-02 2.719 0.00656 **
Gendermale 1.300e+00 3.668e+00 8.109e-01 1.603 0.10901
Hypertension.compositeno -1.825e+01 1.186e-08 6.198e+03 -0.003 0.99765
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes 6.644e-01 1.943e+00 6.672e-01 0.996 0.31935
SmokerCurrentno -4.135e-01 6.613e-01 5.625e-01 -0.735 0.46231
SmokerCurrentyes NA NA 0.000e+00 NA NA
Med.Statin.LLDno 6.205e-01 1.860e+00 5.885e-01 1.054 0.29169
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 5.564e-01 1.744e+00 7.174e-01 0.775 0.43805
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD -2.473e-02 9.756e-01 1.398e-02 -1.769 0.07689 .
BMI 3.300e-02 1.034e+00 8.036e-02 0.411 0.68131
CAD_history 1.119e-01 1.118e+00 5.550e-01 0.202 0.84020
Stroke_history 6.650e-02 1.069e+00 5.801e-01 0.115 0.90875
Peripheral.interv 1.094e-01 1.116e+00 7.207e-01 0.152 0.87939
stenose0-49% -1.205e-01 8.865e-01 4.082e+04 0.000 1.00000
stenose50-70% 6.892e-01 1.992e+00 2.304e+04 0.000 0.99998
stenose70-90% 1.854e+01 1.126e+08 2.053e+04 0.001 0.99928
stenose90-99% 1.825e+01 8.401e+07 2.053e+04 0.001 0.99929
stenose100% (Occlusion) NA NA 0.000e+00 NA NA
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% NA NA 0.000e+00 NA NA
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
IL6_pg_ug_2015_rank 1.903e-01 1.210e+00 2.990e-01 0.636 0.52458
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00474,3.10694] 5.858e-01 1.707e+00 0.1918 1.789
Age 1.128e+00 8.866e-01 1.0342 1.230
Gendermale 3.668e+00 2.727e-01 0.7485 17.973
Hypertension.compositeno 1.186e-08 8.434e+07 0.0000 Inf
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 1.943e+00 5.146e-01 0.5256 7.185
SmokerCurrentno 6.613e-01 1.512e+00 0.2196 1.992
SmokerCurrentyes NA NA NA NA
Med.Statin.LLDno 1.860e+00 5.377e-01 0.5869 5.894
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 1.744e+00 5.733e-01 0.4275 7.117
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 9.756e-01 1.025e+00 0.9492 1.003
BMI 1.034e+00 9.675e-01 0.8829 1.210
CAD_history 1.118e+00 8.941e-01 0.3768 3.319
Stroke_history 1.069e+00 9.357e-01 0.3428 3.332
Peripheral.interv 1.116e+00 8.964e-01 0.2716 4.581
stenose0-49% 8.865e-01 1.128e+00 0.0000 Inf
stenose50-70% 1.992e+00 5.020e-01 0.0000 Inf
stenose70-90% 1.126e+08 8.880e-09 0.0000 Inf
stenose90-99% 8.401e+07 1.190e-08 0.0000 Inf
stenose100% (Occlusion) NA NA NA NA
stenoseNA NA NA NA NA
stenose50-99% NA NA NA NA
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
IL6_pg_ug_2015_rank 1.210e+00 8.267e-01 0.6731 2.173
Concordance= 0.874 (se = 0.028 )
Likelihood ratio test= 31.1 on 18 df, p=0.03
Wald test = 7.85 on 18 df, p=1
Score (logrank) test = 27.82 on 18 df, p=0.06
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' IL6_rank ' and its association to ' epcvdeath.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epcvdeath.3years
Protein...................: IL6_rank
Effect size...............: -0.534802
Standard error............: 0.569715
Odds ratio (effect size)..: 0.586
Lower 95% CI..............: 0.192
Upper 95% CI..............: 1.789
T-value...................: -0.938719
P-value...................: 0.3478749
Sample size in model......: 368
Number of events..........: 16
> processing [MCP1_rank]; 2 out of 5 proteins.
> cross tabulation of MCP1_rank-stratum.
[-2.41053,0.00444) [ 0.00444,3.12635]
283 282
> fitting the model for MCP1_rank-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + IL6_pg_ug_2015_rank, data = TEMP.DF)
n= 392, number of events= 17
(1996 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00444,3.12635] -3.168e-01 7.285e-01 5.580e-01 -0.568 0.57023
Age 1.065e-01 1.112e+00 4.140e-02 2.572 0.01012 *
Gendermale 1.261e+00 3.527e+00 7.908e-01 1.594 0.11092
Hypertension.compositeno -1.930e+01 4.153e-09 8.188e+03 -0.002 0.99812
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes 5.345e-01 1.707e+00 6.662e-01 0.802 0.42240
SmokerCurrentno -4.488e-01 6.384e-01 5.490e-01 -0.817 0.41367
SmokerCurrentyes NA NA 0.000e+00 NA NA
Med.Statin.LLDno 7.274e-01 2.070e+00 5.707e-01 1.275 0.20245
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 3.782e-01 1.460e+00 7.117e-01 0.531 0.59515
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD -3.813e-02 9.626e-01 1.433e-02 -2.662 0.00778 **
BMI 1.193e-02 1.012e+00 7.964e-02 0.150 0.88096
CAD_history -3.662e-01 6.934e-01 5.744e-01 -0.637 0.52382
Stroke_history 1.794e-01 1.196e+00 5.583e-01 0.321 0.74803
Peripheral.interv 6.105e-01 1.841e+00 6.483e-01 0.942 0.34637
stenose0-49% -5.579e-01 5.724e-01 7.733e+04 0.000 0.99999
stenose50-70% 6.658e-01 1.946e+00 4.168e+04 0.000 0.99999
stenose70-90% 1.967e+01 3.486e+08 3.799e+04 0.001 0.99959
stenose90-99% 1.929e+01 2.375e+08 3.799e+04 0.001 0.99960
stenose100% (Occlusion) NA NA 0.000e+00 NA NA
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% NA NA 0.000e+00 NA NA
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
IL6_pg_ug_2015_rank 9.400e-02 1.099e+00 2.942e-01 0.319 0.74936
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00444,3.12635] 7.285e-01 1.373e+00 0.2440 2.175
Age 1.112e+00 8.990e-01 1.0256 1.206
Gendermale 3.527e+00 2.835e-01 0.7487 16.619
Hypertension.compositeno 4.153e-09 2.408e+08 0.0000 Inf
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 1.707e+00 5.860e-01 0.4624 6.298
SmokerCurrentno 6.384e-01 1.566e+00 0.2177 1.872
SmokerCurrentyes NA NA NA NA
Med.Statin.LLDno 2.070e+00 4.831e-01 0.6763 6.335
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 1.460e+00 6.851e-01 0.3618 5.889
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 9.626e-01 1.039e+00 0.9359 0.990
BMI 1.012e+00 9.881e-01 0.8657 1.183
CAD_history 6.934e-01 1.442e+00 0.2249 2.138
Stroke_history 1.196e+00 8.358e-01 0.4005 3.574
Peripheral.interv 1.841e+00 5.431e-01 0.5167 6.562
stenose0-49% 5.724e-01 1.747e+00 0.0000 Inf
stenose50-70% 1.946e+00 5.138e-01 0.0000 Inf
stenose70-90% 3.486e+08 2.868e-09 0.0000 Inf
stenose90-99% 2.375e+08 4.210e-09 0.0000 Inf
stenose100% (Occlusion) NA NA NA NA
stenoseNA NA NA NA NA
stenose50-99% NA NA NA NA
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
IL6_pg_ug_2015_rank 1.099e+00 9.103e-01 0.6171 1.956
Concordance= 0.89 (se = 0.023 )
Likelihood ratio test= 35.41 on 18 df, p=0.008
Wald test = 6.69 on 18 df, p=1
Score (logrank) test = 31.24 on 18 df, p=0.03
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_rank ' and its association to ' epcvdeath.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epcvdeath.3years
Protein...................: MCP1_rank
Effect size...............: -0.316806
Standard error............: 0.55804
Odds ratio (effect size)..: 0.728
Lower 95% CI..............: 0.244
Upper 95% CI..............: 2.175
T-value...................: -0.567713
P-value...................: 0.5702299
Sample size in model......: 392
Number of events..........: 17
> processing [IL6_pg_ug_2015_rank]; 3 out of 5 proteins.
> cross tabulation of IL6_pg_ug_2015_rank-stratum.
[-3.33061,0.00109) [ 0.00109,3.33061]
577 577
> fitting the model for IL6_pg_ug_2015_rank-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + IL6_pg_ug_2015_rank, data = TEMP.DF)
n= 1002, number of events= 35
(1386 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00109,3.33061] 3.124e-01 1.367e+00 3.495e-01 0.894 0.371352
Age 6.928e-02 1.072e+00 2.589e-02 2.676 0.007450 **
Gendermale 1.013e+00 2.755e+00 4.933e-01 2.054 0.039942 *
Hypertension.compositeno -1.767e+01 2.123e-08 3.770e+03 -0.005 0.996261
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes -5.545e-02 9.461e-01 4.082e-01 -0.136 0.891958
SmokerCurrentno -4.117e-01 6.625e-01 3.828e-01 -1.076 0.282106
SmokerCurrentyes NA NA 0.000e+00 NA NA
Med.Statin.LLDno 3.902e-02 1.040e+00 4.239e-01 0.092 0.926647
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 1.096e+00 2.991e+00 3.963e-01 2.765 0.005696 **
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD -3.121e-02 9.693e-01 9.102e-03 -3.428 0.000607 ***
BMI 6.702e-02 1.069e+00 5.147e-02 1.302 0.192850
CAD_history 2.453e-01 1.278e+00 3.548e-01 0.692 0.489238
Stroke_history -1.405e-01 8.689e-01 3.867e-01 -0.363 0.716259
Peripheral.interv 6.223e-01 1.863e+00 4.073e-01 1.528 0.126503
stenose0-49% -1.957e+01 3.162e-09 2.801e+04 -0.001 0.999442
stenose50-70% -8.921e-01 4.098e-01 1.238e+00 -0.720 0.471253
stenose70-90% -1.380e+00 2.515e-01 1.065e+00 -1.296 0.195019
stenose90-99% -8.273e-01 4.372e-01 1.042e+00 -0.794 0.427146
stenose100% (Occlusion) -1.912e+01 4.954e-09 1.969e+04 -0.001 0.999225
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% -1.913e+01 4.932e-09 4.806e+04 0.000 0.999682
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
IL6_pg_ug_2015_rank[ 0.00109,3.33061] NA NA 0.000e+00 NA NA
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00109,3.33061] 1.367e+00 7.317e-01 0.68899 2.7110
Age 1.072e+00 9.331e-01 1.01871 1.1275
Gendermale 2.755e+00 3.630e-01 1.04766 7.2440
Hypertension.compositeno 2.123e-08 4.709e+07 0.00000 Inf
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 9.461e-01 1.057e+00 0.42503 2.1058
SmokerCurrentno 6.625e-01 1.509e+00 0.31288 1.4029
SmokerCurrentyes NA NA NA NA
Med.Statin.LLDno 1.040e+00 9.617e-01 0.45305 2.3865
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 2.991e+00 3.343e-01 1.37565 6.5034
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 9.693e-01 1.032e+00 0.95214 0.9867
BMI 1.069e+00 9.352e-01 0.96671 1.1828
CAD_history 1.278e+00 7.824e-01 0.63761 2.5618
Stroke_history 8.689e-01 1.151e+00 0.40722 1.8539
Peripheral.interv 1.863e+00 5.367e-01 0.83869 4.1396
stenose0-49% 3.162e-09 3.162e+08 0.00000 Inf
stenose50-70% 4.098e-01 2.440e+00 0.03619 4.6407
stenose70-90% 2.515e-01 3.976e+00 0.03118 2.0286
stenose90-99% 4.372e-01 2.287e+00 0.05675 3.3690
stenose100% (Occlusion) 4.954e-09 2.018e+08 0.00000 Inf
stenoseNA NA NA NA NA
stenose50-99% 4.932e-09 2.028e+08 0.00000 Inf
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
IL6_pg_ug_2015_rank[ 0.00109,3.33061] NA NA NA NA
Concordance= 0.841 (se = 0.028 )
Likelihood ratio test= 59.89 on 19 df, p=4e-06
Wald test = 21.5 on 19 df, p=0.3
Score (logrank) test = 56.6 on 19 df, p=1e-05
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' IL6_pg_ug_2015_rank ' and its association to ' epcvdeath.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epcvdeath.3years
Protein...................: IL6_pg_ug_2015_rank
Effect size...............: 0.312398
Standard error............: 0.349461
Odds ratio (effect size)..: 1.367
Lower 95% CI..............: 0.689
Upper 95% CI..............: 2.711
T-value...................: 0.893944
P-value...................: 0.3713521
Sample size in model......: 1002
Number of events..........: 35
> processing [IL6R_pg_ug_2015_rank]; 4 out of 5 proteins.
> cross tabulation of IL6R_pg_ug_2015_rank-stratum.
[-3.33109,0.00108) [ 0.00108,3.33109]
578 578
> fitting the model for IL6R_pg_ug_2015_rank-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + IL6_pg_ug_2015_rank, data = TEMP.DF)
n= 970, number of events= 35
(1418 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00108,3.33109] 4.835e-01 1.622e+00 3.875e-01 1.248 0.21215
Age 6.968e-02 1.072e+00 2.613e-02 2.667 0.00766 **
Gendermale 9.835e-01 2.674e+00 4.923e-01 1.998 0.04576 *
Hypertension.compositeno -1.769e+01 2.076e-08 3.845e+03 -0.005 0.99633
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes 1.194e-02 1.012e+00 4.099e-01 0.029 0.97676
SmokerCurrentno -3.921e-01 6.757e-01 3.837e-01 -1.022 0.30689
SmokerCurrentyes NA NA 0.000e+00 NA NA
Med.Statin.LLDno -4.238e-02 9.585e-01 4.273e-01 -0.099 0.92100
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 1.090e+00 2.973e+00 3.960e-01 2.752 0.00593 **
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD -2.995e-02 9.705e-01 9.116e-03 -3.285 0.00102 **
BMI 6.719e-02 1.069e+00 5.221e-02 1.287 0.19817
CAD_history 2.806e-01 1.324e+00 3.558e-01 0.789 0.43026
Stroke_history -1.626e-01 8.499e-01 3.863e-01 -0.421 0.67387
Peripheral.interv 5.694e-01 1.767e+00 4.049e-01 1.406 0.15964
stenose0-49% -1.927e+01 4.275e-09 3.249e+04 -0.001 0.99953
stenose50-70% -9.251e-01 3.965e-01 1.238e+00 -0.747 0.45499
stenose70-90% -1.478e+00 2.280e-01 1.066e+00 -1.386 0.16572
stenose90-99% -9.727e-01 3.781e-01 1.049e+00 -0.927 0.35383
stenose100% (Occlusion) -1.925e+01 4.363e-09 2.128e+04 -0.001 0.99928
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% -1.928e+01 4.238e-09 4.922e+04 0.000 0.99969
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
IL6_pg_ug_2015_rank[ 0.00109,3.33061] 1.342e-01 1.144e+00 3.690e-01 0.364 0.71610
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00108,3.33109] 1.622e+00 6.166e-01 0.75879 3.466
Age 1.072e+00 9.327e-01 1.01864 1.128
Gendermale 2.674e+00 3.740e-01 1.01868 7.018
Hypertension.compositeno 2.076e-08 4.816e+07 0.00000 Inf
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 1.012e+00 9.881e-01 0.45320 2.260
SmokerCurrentno 6.757e-01 1.480e+00 0.31850 1.433
SmokerCurrentyes NA NA NA NA
Med.Statin.LLDno 9.585e-01 1.043e+00 0.41481 2.215
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 2.973e+00 3.363e-01 1.36823 6.462
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 9.705e-01 1.030e+00 0.95331 0.988
BMI 1.069e+00 9.350e-01 0.96546 1.185
CAD_history 1.324e+00 7.553e-01 0.65921 2.659
Stroke_history 8.499e-01 1.177e+00 0.39860 1.812
Peripheral.interv 1.767e+00 5.659e-01 0.79917 3.908
stenose0-49% 4.275e-09 2.339e+08 0.00000 Inf
stenose50-70% 3.965e-01 2.522e+00 0.03502 4.489
stenose70-90% 2.280e-01 4.385e+00 0.02820 1.844
stenose90-99% 3.781e-01 2.645e+00 0.04837 2.955
stenose100% (Occlusion) 4.363e-09 2.292e+08 0.00000 Inf
stenoseNA NA NA NA NA
stenose50-99% 4.238e-09 2.360e+08 0.00000 Inf
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
IL6_pg_ug_2015_rank[ 0.00109,3.33061] 1.144e+00 8.744e-01 0.55483 2.357
Concordance= 0.838 (se = 0.03 )
Likelihood ratio test= 60.45 on 20 df, p=6e-06
Wald test = 22.49 on 20 df, p=0.3
Score (logrank) test = 58.56 on 20 df, p=1e-05
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' IL6R_pg_ug_2015_rank ' and its association to ' epcvdeath.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epcvdeath.3years
Protein...................: IL6R_pg_ug_2015_rank
Effect size...............: 0.483527
Standard error............: 0.387539
Odds ratio (effect size)..: 1.622
Lower 95% CI..............: 0.759
Upper 95% CI..............: 3.466
T-value...................: 1.247686
P-value...................: 0.2121461
Sample size in model......: 970
Number of events..........: 35
> processing [MCP1_pg_ug_2015_rank]; 5 out of 5 proteins.
> cross tabulation of MCP1_pg_ug_2015_rank-stratum.
[-3.34148,0.00104) [ 0.00104,3.34148]
600 600
> fitting the model for MCP1_pg_ug_2015_rank-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + CAD_history + Stroke_history + Peripheral.interv +
stenose + IL6_pg_ug_2015_rank, data = TEMP.DF)
n= 1001, number of events= 35
(1387 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00104,3.34148] -1.311e-01 8.771e-01 3.603e-01 -0.364 0.715928
Age 6.942e-02 1.072e+00 2.601e-02 2.669 0.007604 **
Gendermale 1.020e+00 2.774e+00 4.935e-01 2.067 0.038694 *
Hypertension.compositeno -1.766e+01 2.148e-08 3.772e+03 -0.005 0.996265
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes -6.257e-02 9.393e-01 4.085e-01 -0.153 0.878241
SmokerCurrentno -4.086e-01 6.646e-01 3.830e-01 -1.067 0.285998
SmokerCurrentyes NA NA 0.000e+00 NA NA
Med.Statin.LLDno 3.815e-02 1.039e+00 4.251e-01 0.090 0.928495
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 1.103e+00 3.012e+00 3.970e-01 2.777 0.005479 **
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD -3.125e-02 9.692e-01 9.152e-03 -3.415 0.000638 ***
BMI 6.593e-02 1.068e+00 5.110e-02 1.290 0.197019
CAD_history 2.433e-01 1.275e+00 3.554e-01 0.685 0.493616
Stroke_history -1.326e-01 8.758e-01 3.875e-01 -0.342 0.732094
Peripheral.interv 6.167e-01 1.853e+00 4.071e-01 1.515 0.129810
stenose0-49% -1.966e+01 2.886e-09 2.782e+04 -0.001 0.999436
stenose50-70% -9.291e-01 3.949e-01 1.242e+00 -0.748 0.454465
stenose70-90% -1.429e+00 2.395e-01 1.073e+00 -1.331 0.183035
stenose90-99% -8.774e-01 4.159e-01 1.051e+00 -0.835 0.403982
stenose100% (Occlusion) -1.920e+01 4.589e-09 1.973e+04 -0.001 0.999224
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% -1.909e+01 5.110e-09 4.798e+04 0.000 0.999683
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
IL6_pg_ug_2015_rank[ 0.00109,3.33061] 3.461e-01 1.414e+00 3.645e-01 0.949 0.342382
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00104,3.34148] 8.771e-01 1.140e+00 0.43288 1.7773
Age 1.072e+00 9.329e-01 1.01862 1.1279
Gendermale 2.774e+00 3.605e-01 1.05447 7.2981
Hypertension.compositeno 2.148e-08 4.655e+07 0.00000 Inf
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 9.393e-01 1.065e+00 0.42184 2.0917
SmokerCurrentno 6.646e-01 1.505e+00 0.31375 1.4077
SmokerCurrentyes NA NA NA NA
Med.Statin.LLDno 1.039e+00 9.626e-01 0.45157 2.3901
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 3.012e+00 3.320e-01 1.38341 6.5594
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 9.692e-01 1.032e+00 0.95200 0.9868
BMI 1.068e+00 9.362e-01 0.96635 1.1807
CAD_history 1.275e+00 7.840e-01 0.63554 2.5597
Stroke_history 8.758e-01 1.142e+00 0.40980 1.8716
Peripheral.interv 1.853e+00 5.397e-01 0.83427 4.1144
stenose0-49% 2.886e-09 3.465e+08 0.00000 Inf
stenose50-70% 3.949e-01 2.532e+00 0.03461 4.5063
stenose70-90% 2.395e-01 4.175e+00 0.02922 1.9633
stenose90-99% 4.159e-01 2.405e+00 0.05297 3.2650
stenose100% (Occlusion) 4.589e-09 2.179e+08 0.00000 Inf
stenoseNA NA NA NA NA
stenose50-99% 5.110e-09 1.957e+08 0.00000 Inf
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
IL6_pg_ug_2015_rank[ 0.00109,3.33061] 1.414e+00 7.074e-01 0.69187 2.8881
Concordance= 0.841 (se = 0.028 )
Likelihood ratio test= 60.03 on 20 df, p=7e-06
Wald test = 21.58 on 20 df, p=0.4
Score (logrank) test = 56.64 on 20 df, p=2e-05
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_pg_ug_2015_rank ' and its association to ' epcvdeath.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epcvdeath.3years
Protein...................: MCP1_pg_ug_2015_rank
Effect size...............: -0.131118
Standard error............: 0.360306
Odds ratio (effect size)..: 0.877
Lower 95% CI..............: 0.433
Upper 95% CI..............: 1.777
T-value...................: -0.363906
P-value...................: 0.715928
Sample size in model......: 1001
Number of events..........: 35 
cat("- Edit the column names...\n")- Edit the column names...colnames(COX.results) = c("Dataset", "Outcome", "CpG",
"Beta", "s.e.m.",
"HR", "low95CI", "up95CI",
"Z-value", "P-value", "SampleSize", "N_events")
cat("- Correct the variable types...\n")- Correct the variable types...COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)
AEDB.CEA.COX.results <- COX.results
# Save the data
cat("- Writing results to Excel-file...\n")- Writing results to Excel-file...head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AEDB.CEA.COX.results,
file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Cox.2G.MODEL4.xlsx"),
creator = "Sander W. van der Laan",
sheetName = "Results", headerStyle = head.style,
row.names = FALSE, col.names = TRUE, overwrite = TRUE)
# Removing intermediates
cat("- Removing intermediate files...\n")- Removing intermediate files...rm(TEMP.DF, protein, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AEDB.CEA.COX.results)
rm(head.style)object 'head.style' not foundCorrelations
All biomarkers
We correlated serum and plaque levels of the biomarkers.
# Installation of ggcorrplot()
# --------------------------------
if(!require(devtools))
install.packages("devtools")
devtools::install_github("kassambara/ggcorrplot")Skipping install of 'ggcorrplot' from a github remote, the SHA1 (c46b4cce) has not changed since last install.
Use `force = TRUE` to force installationlibrary(ggcorrplot)
# Creating matrix - natural log transformed
# --------------------------------
AEDB.CEA.temp <- subset(AEDB.CEA,
select = c("IL6_LN", "MCP1_LN", "IL6_pg_ug_2015_LN", "MCP1_pg_ug_2015_LN", "IL6R_pg_ug_2015_LN",
TRAITS.BIN, TRAITS.CON)
)
AEDB.CEA.temp$CalcificationPlaque <- as.numeric(AEDB.CEA.temp$CalcificationPlaque)
AEDB.CEA.temp$CollagenPlaque <- as.numeric(AEDB.CEA.temp$CollagenPlaque)
AEDB.CEA.temp$Fat10Perc <- as.numeric(AEDB.CEA.temp$Fat10Perc)
AEDB.CEA.temp$IPH <- as.numeric(AEDB.CEA.temp$IPH)
str(AEDB.CEA.temp)Classes ‘tbl_df’, ‘tbl’ and 'data.frame': 2388 obs. of 12 variables:
$ IL6_LN : num 3.97 4.56 4.58 4.93 NA ...
$ MCP1_LN : num 3.7 5.53 4.35 4.19 NA ...
$ IL6_pg_ug_2015_LN : num -5.23 -7.65 -2 NA NA ...
$ MCP1_pg_ug_2015_LN : num -0.0505 NA 0.8335 NA NA ...
$ IL6R_pg_ug_2015_LN : num 0.0893 -8.8939 -0.3091 NA NA ...
$ CalcificationPlaque: num 1 2 2 2 1 1 2 1 1 2 ...
$ CollagenPlaque : num 2 2 2 2 2 2 1 1 2 1 ...
$ Fat10Perc : num 2 2 1 1 2 2 2 2 2 2 ...
$ IPH : num 2 1 2 2 2 2 2 2 1 2 ...
$ Macrophages_LN : num 0.542 -1.732 -1.386 -0.426 -4.605 ...
$ SMC_LN : num 1.411 1.833 0.965 0.55 2.239 ...
$ VesselDensity_LN : num 0.846 1.203 2.46 2.398 1.541 ...AEDB.CEA.matrix <- as.matrix(AEDB.CEA.temp)
rm(AEDB.CEA.temp)
corr_biomarkers <- round(cor(AEDB.CEA.matrix,
use = "pairwise.complete.obs", #the correlation or covariance between each pair of variables is computed using all complete pairs of observations on those variables
method = "spearman"), 3)
# corr_biomarkers
corr_biomarkers_p <- ggcorrplot::cor_pmat(AEDB.CEA.matrix, use = "pairwise.complete.obs", method = "spearman")Cannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with ties# Add correlation coefficients
# --------------------------------
# argument lab = TRUE
ggcorrplot(corr_biomarkers,
method = "square",
type = "lower",
title = "Cross biomarker correlations",
show.legend = TRUE, legend.title = bquote("Spearman's"~italic(rho)),
ggtheme = ggplot2::theme_minimal, outline.color = "#FFFFFF",
show.diag = TRUE,
hc.order = FALSE,
lab = FALSE,
digits = 3,
# p.mat = corr_biomarkers_p, sig.level = 0.05,
colors = c("#1290D9", "#FFFFFF", "#E55738"))
# Creating matrix - inverse-rank transformation
# --------------------------------
AEDB.CEA.temp <- subset(AEDB.CEA,
select = c("IL6_rank", "MCP1_rank", "IL6_pg_ug_2015_rank", "MCP1_pg_ug_2015_rank", "IL6R_pg_ug_2015_rank",
TRAITS.BIN, TRAITS.CON.RANK)
)
AEDB.CEA.temp$CalcificationPlaque <- as.numeric(AEDB.CEA.temp$CalcificationPlaque)
AEDB.CEA.temp$CollagenPlaque <- as.numeric(AEDB.CEA.temp$CollagenPlaque)
AEDB.CEA.temp$Fat10Perc <- as.numeric(AEDB.CEA.temp$Fat10Perc)
AEDB.CEA.temp$IPH <- as.numeric(AEDB.CEA.temp$IPH)
str(AEDB.CEA.temp)Classes ‘tbl_df’, ‘tbl’ and 'data.frame': 2388 obs. of 12 variables:
$ IL6_rank : num 0.205 0.703 0.746 1.085 NA ...
$ MCP1_rank : num -0.944 1.196 -0.297 -0.499 NA ...
$ IL6_pg_ug_2015_rank : num -1.422 -2.744 0.919 NA NA ...
$ MCP1_pg_ug_2015_rank: num 0.936 NA 1.691 NA NA ...
$ IL6R_pg_ug_2015_rank: num 2.21 -2.85 1.78 NA NA ...
$ CalcificationPlaque : num 1 2 2 2 1 1 2 1 1 2 ...
$ CollagenPlaque : num 2 2 2 2 2 2 1 1 2 1 ...
$ Fat10Perc : num 2 2 1 1 2 2 2 2 2 2 ...
$ IPH : num 2 1 2 2 2 2 2 2 1 2 ...
$ Macrophages_rank : num 1.12 -0.276 -0.105 0.403 -1.414 ...
$ SMC_rank : num 1.134 1.678 0.625 0.256 2.086 ...
$ VesselDensity_rank : num -0.976 -0.773 0.713 0.609 -0.529 ...AEDB.CEA.matrix.RANK <- as.matrix(AEDB.CEA.temp)
rm(AEDB.CEA.temp)
corr_biomarkers.rank <- round(cor(AEDB.CEA.matrix.RANK,
use = "pairwise.complete.obs", #the correlation or covariance between each pair of variables is computed using all complete pairs of observations on those variables
method = "spearman"), 3)
# corr_biomarkers.rank
corr_biomarkers_p.rank <- ggcorrplot::cor_pmat(AEDB.CEA.matrix.RANK, use = "pairwise.complete.obs", method = "spearman")Cannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with ties# Add correlation coefficients
# --------------------------------
# argument lab = TRUE
ggcorrplot(corr_biomarkers.rank,
method = "square",
type = "lower",
title = "Cross biomarker correlations",
show.legend = TRUE, legend.title = bquote("Spearman's"~italic(rho)),
ggtheme = ggplot2::theme_minimal, outline.color = "#FFFFFF",
show.diag = TRUE,
hc.order = FALSE,
lab = FALSE,
digits = 3,
# p.mat = corr_biomarkers_p.rank, sig.level = 0.05,
colors = c("#1290D9", "#FFFFFF", "#E55738"))
# flattenCorrMatrix
# --------------------------------
# cormat : matrix of the correlation coefficients
# pmat : matrix of the correlation p-values
flattenCorrMatrix <- function(cormat, pmat) {
ut <- upper.tri(cormat)
data.frame(
biomarker_row = rownames(cormat)[row(cormat)[ut]],
biomarker_column = rownames(cormat)[col(cormat)[ut]],
spearman_cor =(cormat)[ut],
pval = pmat[ut]
)
}
corr_biomarkers.df <- as.data.table(flattenCorrMatrix(corr_biomarkers, corr_biomarkers_p))
DT::datatable(corr_biomarkers.df)
corr_biomarkers.rank.df <- as.data.table(flattenCorrMatrix(corr_biomarkers.rank, corr_biomarkers_p.rank))
DT::datatable(corr_biomarkers.rank.df)
# chart of a correlation matrix
# --------------------------------
# Alternative solution https://www.r-graph-gallery.com/199-correlation-matrix-with-ggally.html
install.packages.auto("PerformanceAnalytics")
chart.Correlation.new <- function (R, histogram = TRUE, method = c("pearson", "kendall",
"spearman"), ...)
{
x = checkData(R, method = "matrix")
if (missing(method))
method = method[1]
cormeth <- method
panel.cor <- function(x, y, digits = 2, prefix = "", use = "pairwise.complete.obs",
method = cormeth, cex.cor, ...) {
usr <- par("usr")
on.exit(par(usr))
par(usr = c(0, 1, 0, 1))
r <- cor(x, y, use = use, method = method)
txt <- format(c(r, 0.123456789), digits = digits)[1]
txt <- paste(prefix, txt, sep = "")
if (missing(cex.cor))
cex <- 0.8/strwidth(txt)
test <- cor.test(as.numeric(x), as.numeric(y), method = method)
Signif <- symnum(test$p.value, corr = FALSE, na = FALSE,
cutpoints = c(0, 0.001, 0.01, 0.05, 0.1, 1), symbols = c("***",
"**", "*", ".", " "))
text(0.5, 0.5, txt, cex = cex * (abs(r) + 0.3)/1.3)
text(0.8, 0.8, Signif, cex = cex, col = 2)
}
f <- function(t) {
dnorm(t, mean = mean(x), sd = sd.xts(x))
}
dotargs <- list(...)
dotargs$method <- NULL
rm(method)
hist.panel = function(x, ... = NULL) {
par(new = TRUE)
hist(x, col = "#1290D9", probability = TRUE, axes = FALSE,
# hist(x, col = "light gray", probability = TRUE, axes = FALSE,
main = "", breaks = "FD")
lines(density(x, na.rm = TRUE), col = "#E55738", lwd = 1)
rug(x)
}
if (histogram)
pairs(x, gap = 0, lower.panel = panel.smooth, upper.panel = panel.cor,
diag.panel = hist.panel, ...)
else pairs(x, gap = 0, lower.panel = panel.smooth, upper.panel = panel.cor, ...)
}
chart.Correlation.new(AEDB.CEA.matrix, method = "spearman", histogram = TRUE, pch = 3)
chart.Correlation.new(AEDB.CEA.matrix.RANK, method = "spearman", histogram = TRUE, pch = 3)
# alternative chart of a correlation matrix
# --------------------------------
# Alternative solution https://www.r-graph-gallery.com/199-correlation-matrix-with-ggally.html
install.packages.auto("GGally")
# Quick display of two cabapilities of GGally, to assess the distribution and correlation of variables
library(GGally)
# From the help page:
ggpairs(AEDB.CEA,
columns = c("IL6_rank", "MCP1_rank", "IL6_pg_ug_2015_rank", "MCP1_pg_ug_2015_rank", "IL6R_pg_ug_2015_rank", TRAITS.BIN, TRAITS.CON.RANK),
columnLabels = c("IL6 (serum)", "MCP1 (serum)", "IL6", "MCP1", "IL6R",
"Calcification", "Collagen", "Fat 10%", "IPH", "Macrophages", "SMC", "Vessel density"),
method = c("spearman"),
# ggplot2::aes(colour = Gender),
progress = FALSE) Extra arguments: 'method' are being ignored. If these are meant to be aesthetics, submit them using the 'mapping' variable within ggpairs with ggplot2::aes or ggplot2::aes_string.
Circulating MCP1
Finally, we explored in a sub-sample, where circulating MCP-1 levels are available, the following:
- A correlation between MCP-1 levels in the plaque and circulating MCP-1 levels
- Associations of circulating MCP-1 levels with plaque vulnerability characteristics
- Associations of circulating MCP-1 levels with the status of the plaque in terms of presence of symptoms (symptomatic vs. asymptomatic)
- Associations of circulating MCP-1 levels with the primary composite endpoint of secondary cardiovascular events.
# Installation of ggcorrplot()
# --------------------------------
if(!require(devtools))
install.packages("devtools")
devtools::install_github("kassambara/ggcorrplot")Skipping install of 'ggcorrplot' from a github remote, the SHA1 (c46b4cce) has not changed since last install.
Use `force = TRUE` to force installationlibrary(ggcorrplot)
# Creating matrix - natural log transformed
# --------------------------------
AEDB.CEA.temp <- subset(AEDB.CEA,
select = c("MCP1_LN",
TRAITS.BIN, TRAITS.CON,
"Symptoms.5G", "AsymptSympt", "EP_major", "EP_composite")
)
AEDB.CEA.temp$CalcificationPlaque <- as.numeric(AEDB.CEA.temp$CalcificationPlaque)
AEDB.CEA.temp$CollagenPlaque <- as.numeric(AEDB.CEA.temp$CollagenPlaque)
AEDB.CEA.temp$Fat10Perc <- as.numeric(AEDB.CEA.temp$Fat10Perc)
AEDB.CEA.temp$IPH <- as.numeric(AEDB.CEA.temp$IPH)
AEDB.CEA.temp$Symptoms.5G <- as.numeric(AEDB.CEA.temp$Symptoms.5G)
AEDB.CEA.temp$AsymptSympt <- as.numeric(AEDB.CEA.temp$AsymptSympt)
AEDB.CEA.temp$EP_major <- as.numeric(AEDB.CEA.temp$EP_major)
AEDB.CEA.temp$EP_composite <- as.numeric(AEDB.CEA.temp$EP_composite)
# str(AEDB.CEA.temp)
AEDB.CEA.matrix.serum <- as.matrix(AEDB.CEA.temp)
rm(AEDB.CEA.temp)
corr_biomarkers_serum <- round(cor(AEDB.CEA.matrix.serum,
use = "pairwise.complete.obs", #the correlation or covariance between each pair of variables is computed using all complete pairs of observations on those variables
method = "spearman"), 3)
# corr_biomarkers
corr_biomarkers_serum_p <- ggcorrplot::cor_pmat(AEDB.CEA.matrix.serum, use = "pairwise.complete.obs", method = "spearman")Cannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with ties# Add correlation coefficients
# --------------------------------
# argument lab = TRUE
ggcorrplot(corr_biomarkers_serum,
method = "square",
type = "lower",
title = "Cross biomarker correlations",
show.legend = TRUE, legend.title = bquote("Spearman's"~italic(rho)),
ggtheme = ggplot2::theme_minimal, outline.color = "#FFFFFF",
show.diag = TRUE,
hc.order = FALSE,
lab = FALSE,
digits = 3,
# p.mat = corr_biomarkers_serum_p, sig.level = 0.05,
colors = c("#1290D9", "#FFFFFF", "#E55738"))
# Creating matrix - inverse-rank transformation
# --------------------------------
AEDB.CEA.temp <- subset(AEDB.CEA,
select = c("MCP1_rank",
TRAITS.BIN, TRAITS.CON.RANK,
"Symptoms.5G", "AsymptSympt", "EP_major", "EP_composite")
)
AEDB.CEA.temp$CalcificationPlaque <- as.numeric(AEDB.CEA.temp$CalcificationPlaque)
AEDB.CEA.temp$CollagenPlaque <- as.numeric(AEDB.CEA.temp$CollagenPlaque)
AEDB.CEA.temp$Fat10Perc <- as.numeric(AEDB.CEA.temp$Fat10Perc)
AEDB.CEA.temp$IPH <- as.numeric(AEDB.CEA.temp$IPH)
AEDB.CEA.temp$Symptoms.5G <- as.numeric(AEDB.CEA.temp$Symptoms.5G)
AEDB.CEA.temp$AsymptSympt <- as.numeric(AEDB.CEA.temp$AsymptSympt)
AEDB.CEA.temp$EP_major <- as.numeric(AEDB.CEA.temp$EP_major)
AEDB.CEA.temp$EP_composite <- as.numeric(AEDB.CEA.temp$EP_composite)
# str(AEDB.CEA.temp)
AEDB.CEA.matrix.serum.RANK <- as.matrix(AEDB.CEA.temp)
rm(AEDB.CEA.temp)
corr_biomarkers_serum.rank <- round(cor(AEDB.CEA.matrix.serum.RANK,
use = "pairwise.complete.obs", #the correlation or covariance between each pair of variables is computed using all complete pairs of observations on those variables
method = "spearman"), 3)
# corr_biomarkers.rank
corr_biomarkers_serum_p.rank <- ggcorrplot::cor_pmat(AEDB.CEA.matrix.serum.RANK, use = "pairwise.complete.obs", method = "spearman")Cannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with ties# Add correlation coefficients
# --------------------------------
# argument lab = TRUE
ggcorrplot(corr_biomarkers_serum.rank,
method = "square",
type = "lower",
title = "Cross biomarker correlations",
show.legend = TRUE, legend.title = bquote("Spearman's"~italic(rho)),
ggtheme = ggplot2::theme_minimal, outline.color = "#FFFFFF",
show.diag = TRUE,
hc.order = FALSE,
lab = FALSE,
digits = 3,
# p.mat = corr_biomarkers_serum_p.rank, sig.level = 0.05,
colors = c("#1290D9", "#FFFFFF", "#E55738"))
# flattenCorrMatrix
# --------------------------------
# cormat : matrix of the correlation coefficients
# pmat : matrix of the correlation p-values
flattenCorrMatrix <- function(cormat, pmat) {
ut <- upper.tri(cormat)
data.frame(
biomarker_row = rownames(cormat)[row(cormat)[ut]],
biomarker_column = rownames(cormat)[col(cormat)[ut]],
spearman_cor =(cormat)[ut],
pval = pmat[ut]
)
}
corr_biomarkers_serum.df <- as.data.table(flattenCorrMatrix(corr_biomarkers_serum, corr_biomarkers_serum_p))
DT::datatable(corr_biomarkers_serum.df)
corr_biomarkers_serum.rank.df <- as.data.table(flattenCorrMatrix(corr_biomarkers_serum.rank, corr_biomarkers_serum_p.rank))
DT::datatable(corr_biomarkers_serum.rank.df)
# chart of a correlation matrix
# --------------------------------
# Alternative solution https://www.r-graph-gallery.com/199-correlation-matrix-with-ggally.html
install.packages.auto("PerformanceAnalytics")
chart.Correlation.new <- function (R, histogram = TRUE, method = c("pearson", "kendall",
"spearman"), ...)
{
x = checkData(R, method = "matrix")
if (missing(method))
method = method[1]
cormeth <- method
panel.cor <- function(x, y, digits = 2, prefix = "", use = "pairwise.complete.obs",
method = cormeth, cex.cor, ...) {
usr <- par("usr")
on.exit(par(usr))
par(usr = c(0, 1, 0, 1))
r <- cor(x, y, use = use, method = method)
txt <- format(c(r, 0.123456789), digits = digits)[1]
txt <- paste(prefix, txt, sep = "")
if (missing(cex.cor))
cex <- 0.8/strwidth(txt)
test <- cor.test(as.numeric(x), as.numeric(y), method = method)
Signif <- symnum(test$p.value, corr = FALSE, na = FALSE,
cutpoints = c(0, 0.001, 0.01, 0.05, 0.1, 1), symbols = c("***",
"**", "*", ".", " "))
text(0.5, 0.5, txt, cex = cex * (abs(r) + 0.3)/1.3)
text(0.8, 0.8, Signif, cex = cex, col = 2)
}
f <- function(t) {
dnorm(t, mean = mean(x), sd = sd.xts(x))
}
dotargs <- list(...)
dotargs$method <- NULL
rm(method)
hist.panel = function(x, ... = NULL) {
par(new = TRUE)
hist(x, col = "#1290D9", probability = TRUE, axes = FALSE,
# hist(x, col = "light gray", probability = TRUE, axes = FALSE,
main = "", breaks = "FD")
lines(density(x, na.rm = TRUE), col = "#E55738", lwd = 1)
rug(x)
}
if (histogram)
pairs(x, gap = 0, lower.panel = panel.smooth, upper.panel = panel.cor,
diag.panel = hist.panel, ...)
else pairs(x, gap = 0, lower.panel = panel.smooth, upper.panel = panel.cor, ...)
}
chart.Correlation.new(AEDB.CEA.matrix.serum, method = "spearman", histogram = TRUE, pch = 3)
chart.Correlation.new(AEDB.CEA.matrix.serum.RANK, method = "spearman", histogram = TRUE, pch = 3)
# alternative chart of a correlation matrix
# --------------------------------
# Alternative solution https://www.r-graph-gallery.com/199-correlation-matrix-with-ggally.html
install.packages.auto("GGally")
# Quick display of two cabapilities of GGally, to assess the distribution and correlation of variables
library(GGally)
# From the help page:
ggpairs(AEDB.CEA,
columns = c("MCP1_rank", TRAITS.BIN, TRAITS.CON.RANK, "Symptoms.5G", "AsymptSympt", "EP_major", "EP_composite"),
columnLabels = c("MCP1 (serum)",
"Calcification", "Collagen", "Fat 10%", "IPH", "Macrophages", "SMC", "Vessel density",
"Symptoms", "Symptoms (grouped)", "MACE", "Composite"),
method = c("spearman"),
# ggplot2::aes(colour = Gender),
progress = FALSE) Extra arguments: 'method' are being ignored. If these are meant to be aesthetics, submit them using the 'mapping' variable within ggpairs with ggplot2::aes or ggplot2::aes_string.
Session information
Version: v1.0.5
Last update: 2020-03-05
Written by: Sander W. van der Laan (s.w.vanderlaan-2[at]umcutrecht.nl).
Description: Script to select samples from the Ather-Express Biobank Study.
Minimum requirements: R version 3.5.2 (2018-12-20) -- 'Eggshell Igloo', macOS Mojave (10.14.2).sessionInfo()R version 3.6.3 (2020-02-29)
Platform: x86_64-apple-darwin19.3.0 (64-bit)
Running under: macOS Catalina 10.15.3
Matrix products: default
BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /usr/local/Cellar/openblas/0.3.7/lib/libopenblasp-r0.3.7.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] tools stats graphics grDevices utils datasets methods base
other attached packages:
[1] Hmisc_4.3-1 Formula_1.2-3 lattice_0.20-40 survminer_0.4.6 survival_3.1-8
[6] GGally_1.4.0 PerformanceAnalytics_2.0.4 xts_0.12-0 zoo_1.8-7 ggcorrplot_0.1.3.999
[11] labelled_2.2.2 openxlsx_4.1.4 ggpubr_0.2.5.999 magrittr_1.5 tableone_0.11.0
[16] haven_2.2.0 Seurat_3.1.4 devtools_2.2.2 usethis_1.5.1 MASS_7.3-51.5
[21] DT_0.12 knitr_1.28 forcats_0.5.0 stringr_1.4.0 purrr_0.3.3
[26] tibble_2.1.3 ggplot2_3.2.1 tidyverse_1.3.0 data.table_1.12.8 naniar_0.5.0
[31] tidyr_1.0.2 dplyr_0.8.4 optparse_1.6.4 readr_1.3.1
loaded via a namespace (and not attached):
[1] rappdirs_0.3.1 visdat_0.5.3 acepack_1.4.1 irlba_2.3.3 multcomp_1.4-12 rpart_4.1-15
[7] inline_0.3.15 generics_0.0.2 metap_1.3 BiocGenerics_0.30.0 callr_3.4.2 cowplot_1.0.0
[13] TH.data_1.0-10 RANN_2.6.1 future_1.16.0 mutoss_0.1-12 xml2_1.2.2 lubridate_1.7.4
[19] httpuv_1.5.2 StanHeaders_2.19.2 assertthat_0.2.1 xfun_0.12 hms_0.5.3 evaluate_0.14
[25] promises_1.1.0 fansi_0.4.1 caTools_1.18.0 dbplyr_1.4.2 readxl_1.3.1 km.ci_0.5-2
[31] igraph_1.2.4.2 DBI_1.1.0 htmlwidgets_1.5.1 reshape_0.8.8 stats4_3.6.3 ellipsis_0.3.0
[37] crosstalk_1.0.0 backports_1.1.5 survey_3.37 gbRd_0.4-11 RcppParallel_4.4.4 vctrs_0.2.3
[43] Biobase_2.44.0 remotes_2.1.1 ROCR_1.0-7 withr_2.1.2 packrat_0.5.0 checkmate_2.0.0
[49] sctransform_0.2.1 prettyunits_1.1.1 getopt_1.20.3 mnormt_1.5-6 cluster_2.1.0 ape_5.3
[55] lazyeval_0.2.2 crayon_1.3.4 pkgconfig_2.0.3 labeling_0.3 nlme_3.1-145 pkgload_1.0.2
[61] nnet_7.3-13 rlang_0.4.5 globals_0.12.5 lifecycle_0.1.0 sandwich_2.5-1 modelr_0.1.6
[67] rsvd_1.0.3 cellranger_1.1.0 rprojroot_1.3-2 matrixStats_0.55.0 lmtest_0.9-37 Matrix_1.2-18
[73] loo_2.2.0 KMsurv_0.1-5 reprex_0.3.0 base64enc_0.1-3 ggridges_0.5.2 processx_3.4.2
[79] png_0.1-7 viridisLite_0.3.0 bitops_1.0-6 KernSmooth_2.23-16 jpeg_0.1-8.1 ggsignif_0.6.0
[85] scales_1.1.0 memoise_1.1.0 plyr_1.8.6 ica_1.0-2 gplots_3.0.3 bibtex_0.4.2
[91] gdata_2.18.0 compiler_3.6.3 lsei_1.2-0 RColorBrewer_1.1-2 plotrix_3.7-7 fitdistrplus_1.0-14
[97] cli_2.0.2 listenv_0.8.0 patchwork_1.0.0 pbapply_1.4-2 ps_1.3.2 htmlTable_1.13.3
[103] tidyselect_1.0.0 stringi_1.4.6 mitools_2.4 yaml_2.2.1 latticeExtra_0.6-29 ggrepel_0.8.1
[109] survMisc_0.5.5 grid_3.6.3 future.apply_1.4.0 parallel_3.6.3 rstudioapi_0.11 foreign_0.8-76
[115] gridExtra_2.3 farver_2.0.3 Rtsne_0.15 digest_0.6.25 shiny_1.4.0 quadprog_1.5-8
[121] Rcpp_1.0.3 broom_0.5.5 later_1.0.0 RcppAnnoy_0.0.15 httr_1.4.1 rsconnect_0.8.16
[127] npsurv_0.4-0 Rdpack_0.11-1 colorspace_1.4-1 rvest_0.3.5 fs_1.3.1 reticulate_1.14
[133] splines_3.6.3 uwot_0.1.5 sn_1.5-5 multtest_2.40.0 plotly_4.9.2 sessioninfo_1.1.1
[139] xtable_1.8-4 jsonlite_1.6.1 rstan_2.19.3 testthat_2.3.2 R6_2.4.1 TFisher_0.2.0
[145] pillar_1.4.3 htmltools_0.4.0 mime_0.9 glue_1.3.1 fastmap_1.0.1 class_7.3-15
[151] codetools_0.2-16 pkgbuild_1.0.6 tsne_0.1-3 mvtnorm_1.1-0 numDeriv_2016.8-1.1 curl_4.3
[157] leiden_0.3.3 gtools_3.8.1 zip_2.0.4 rmarkdown_2.1 desc_1.2.0 munsell_0.5.0
[163] e1071_1.7-3 reshape2_1.4.3 gtable_0.3.0 Saving environment
save.image(paste0(PROJECT_loc, "/",Today,".",PROJECTNAME,".sample_selection.RData"))| © 1979-2020 Sander W. van der Laan | s.w.vanderlaan-2[at]umcutrecht.nl | swvanderlaan.github.io. |
---
title: "Athero-Express Biobank Study -- IL6 and MCP1 plaque levels."
author: '[Sander W. van der Laan, PhD](https://swvanderlaan.github.io) | @swvanderlaan; Marios Georgakis; Rainer Malik; Martin Dichgans'
date: '`r Sys.Date()`'
output:
  html_notebook:
    cache: yes
    code_folding: hide
    collapse: yes
    df_print: paged
    fig.align: center
    fig_caption: yes
    fig_height: 10
    fig_retina: 2
    fig_width: 12
    theme: paper
    toc: yes
    toc_float:
      collapsed: no
      smooth_scroll: yes
  html_document:
    df_print: paged
    toc: yes
mainfont: Helvetica
subtitle: An 'Athero-Express Biobank Study' project
editor_options:
  chunk_output_type: inline
---
```{r global_options, include = FALSE}
# further define some knitr-options.
knitr::opts_chunk$set(fig.width = 12, fig.height = 8, fig.path = 'Figures/',
                      eval = TRUE, warning = FALSE, message = FALSE)
```

# Preparation

Clean the environment.
```{r ClearEnvironment, include = FALSE}
rm(list = ls())
```

Set locations, and the working directory.
```{r LocalSystem, include = FALSE}
### Operating System Version
### Mac Pro
# ROOT_loc = "/Volumes/EliteProQx2Media"
# GENOMIC_loc = "/Users/svanderlaan/iCloud/Genomics"

### MacBook
ROOT_loc = "/Users/swvanderlaan"
GENOMIC_loc = paste0(ROOT_loc, "/iCloud/Genomics")

### Generic Locations
AEDB_loc = paste0(GENOMIC_loc, "/AE-AAA_GS_DBs")
LAB_loc = paste0(GENOMIC_loc, "/LabBusiness")
RESULTS = paste0(ROOT_loc, "/PLINK/analyses/lookups/AE_20190912_010_MDICHGANS_SWVDLAAN_IL6_MCP1")
RAWDATA = paste0(ROOT_loc, "/PLINK/_AE_ORIGINALS/AESCRNA/prepped_data")
PROJECT_loc = paste0(ROOT_loc, "/PLINK/analyses/lookups/AE_20190912_010_MDICHGANS_SWVDLAAN_IL6_MCP1")

### SOME VARIABLES WE NEED DOWN THE LINE
cat("\nDefining phenotypes and datasets.\n")
PROJECTNAME="IL6MCP1"
# SUBPROJECTNAME=""

cat("\nCreate a new analysis directory, including subdirectories.\n")
# Analysis
ifelse(!dir.exists(file.path(PROJECT_loc, "/",PROJECTNAME)), 
       dir.create(file.path(PROJECT_loc, "/",PROJECTNAME)), 
       FALSE)
ANALYSIS_loc = paste0(PROJECT_loc,"/",PROJECTNAME)

# Plots
ifelse(!dir.exists(file.path(ANALYSIS_loc, "/PLOTS")), 
       dir.create(file.path(ANALYSIS_loc, "/PLOTS")), 
       FALSE)
PLOT_loc = paste0(ANALYSIS_loc,"/PLOTS")

# QC plots
ifelse(!dir.exists(file.path(PLOT_loc, "/QC")), 
       dir.create(file.path(PLOT_loc, "/QC")), 
       FALSE)
QC_loc = paste0(PLOT_loc,"/QC")

# Output files
ifelse(!dir.exists(file.path(ANALYSIS_loc, "/OUTPUT")), 
       dir.create(file.path(ANALYSIS_loc, "/OUTPUT")), 
       FALSE)
OUT_loc = paste0(ANALYSIS_loc, "/OUTPUT")

# COX analysis
ifelse(!dir.exists(file.path(ANALYSIS_loc, "/COX")), 
       dir.create(file.path(ANALYSIS_loc, "/COX")), 
       FALSE)
COX_loc = paste0(ANALYSIS_loc, "/COX")

# Baseline characteristics
ifelse(!dir.exists(file.path(ANALYSIS_loc, "/BASELINE")), 
       dir.create(file.path(ANALYSIS_loc, "/BASELINE")), 
       FALSE)
BASELINE_loc = paste0(ANALYSIS_loc, "/BASELINE")

# Sample selection
ifelse(!dir.exists(file.path(ANALYSIS_loc, "/SELECTIONS")), 
       dir.create(file.path(ANALYSIS_loc, "/SELECTIONS")), 
       FALSE)
SELECTIONS_loc = paste0(ANALYSIS_loc, "/SELECTIONS")

cat("\nSetting working directory and listing its contents.\n")
setwd(paste0(PROJECT_loc))
getwd()
list.files()
```

A package-installation function.
```{r Function: installations, include = FALSE}
install.packages.auto <- function(x) { 
  x <- as.character(substitute(x)) 
  if(isTRUE(x %in% .packages(all.available = TRUE))) { 
    eval(parse(text = sprintf("require(\"%s\")", x)))
  } else { 
    # Update installed packages - this may mean a full upgrade of R, which in turn
    # may not be warrented. 
    # update.install.packages.auto(ask = FALSE) 
    eval(parse(text = sprintf("install.packages(\"%s\", dependencies = TRUE, repos = \"https://cloud.r-project.org/\")", x)))
  }
  if(isTRUE(x %in% .packages(all.available = TRUE))) { 
    eval(parse(text = sprintf("require(\"%s\")", x)))
  } else {
    if (!requireNamespace("BiocManager"))
      install.packages("BiocManager")
    # BiocManager::install() # this would entail updating installed packages, which in turned may not be warrented
    eval(parse(text = sprintf("BiocManager::install(\"%s\")", x)))
    eval(parse(text = sprintf("require(\"%s\")", x)))
  }
}
```

Load those packages.
```{r Setting: loading_packages, include = FALSE}
install.packages.auto("readr")
install.packages.auto("optparse")
install.packages.auto("tools")
install.packages.auto("dplyr")
install.packages.auto("tidyr")
install.packages.auto("naniar")

# To get 'data.table' with 'fwrite' to be able to directly write gzipped-files
# Ref: https://stackoverflow.com/questions/42788401/is-possible-to-use-fwrite-from-data-table-with-gzfile
# install.packages("data.table", repos = "https://Rdatatable.gitlab.io/data.table")
library(data.table)

install.packages.auto("tidyverse")
install.packages.auto("knitr")
install.packages.auto("DT")
install.packages.auto("MASS")
# install.packages.auto("Seurat") # latest version

# Install the devtools package from Hadley Wickham
install.packages.auto('devtools')

install.packages.auto("haven")
install.packages.auto("tableone")

install.packages.auto("ggpubr")

```

We will create a datestamp and define the Utrecht Science Park Colour Scheme.
```{r Setting: Colors, include = FALSE}

Today = format(as.Date(as.POSIXlt(Sys.time())), "%Y%m%d")
Today.Report = format(as.Date(as.POSIXlt(Sys.time())), "%A, %B %d, %Y")

### UtrechtScienceParkColoursScheme
###
### WebsitetoconvertHEXtoRGB:http://hex.colorrrs.com.
### Forsomefunctionsyoushoulddividethesenumbersby255.
### 
###	No.	Color			      HEX	(RGB)						              CHR		  MAF/INFO
###---------------------------------------------------------------------------------------
###	1	  yellow			    #FBB820 (251,184,32)				      =>	1		or 1.0>INFO
###	2	  gold			      #F59D10 (245,157,16)				      =>	2		
###	3	  salmon			    #E55738 (229,87,56)				      =>	3		or 0.05<MAF<0.2 or 0.4<INFO<0.6
###	4	  darkpink		    #DB003F ((219,0,63)				      =>	4		
###	5	  lightpink		    #E35493 (227,84,147)				      =>	5		or 0.8<INFO<1.0
###	6	  pink			      #D5267B (213,38,123)				      =>	6		
###	7	  hardpink		    #CC0071 (204,0,113)				      =>	7		
###	8	  lightpurple	    #A8448A (168,68,138)				      =>	8		
###	9	  purple			    #9A3480 (154,52,128)				      =>	9		
###	10	lavendel		    #8D5B9A (141,91,154)				      =>	10		
###	11	bluepurple		  #705296 (112,82,150)				      =>	11		
###	12	purpleblue		  #686AA9 (104,106,169)			      =>	12		
###	13	lightpurpleblue	#6173AD (97,115,173/101,120,180)	=>	13		
###	14	seablue			    #4C81BF (76,129,191)				      =>	14		
###	15	skyblue			    #2F8BC9 (47,139,201)				      =>	15		
###	16	azurblue		    #1290D9 (18,144,217)				      =>	16		or 0.01<MAF<0.05 or 0.2<INFO<0.4
###	17	lightazurblue	  #1396D8 (19,150,216)				      =>	17		
###	18	greenblue		    #15A6C1 (21,166,193)				      =>	18		
###	19	seaweedgreen	  #5EB17F (94,177,127)				      =>	19		
###	20	yellowgreen		  #86B833 (134,184,51)				      =>	20		
###	21	lightmossgreen	#C5D220 (197,210,32)				      =>	21		
###	22	mossgreen		    #9FC228 (159,194,40)				      =>	22		or MAF>0.20 or 0.6<INFO<0.8
###	23	lightgreen	  	#78B113 (120,177,19)				      =>	23/X
###	24	green			      #49A01D (73,160,29)				      =>	24/Y
###	25	grey			      #595A5C (89,90,92)				        =>	25/XY	or MAF<0.01 or 0.0<INFO<0.2
###	26	lightgrey		    #A2A3A4	(162,163,164)			      =>	26/MT
###
###	ADDITIONAL COLORS
###	27	midgrey			#D7D8D7
###	28	verylightgrey	#ECECEC"
###	29	white			#FFFFFF
###	30	black			#000000
###----------------------------------------------------------------------------------------------

uithof_color = c("#FBB820","#F59D10","#E55738","#DB003F","#E35493","#D5267B",
                 "#CC0071","#A8448A","#9A3480","#8D5B9A","#705296","#686AA9",
                 "#6173AD","#4C81BF","#2F8BC9","#1290D9","#1396D8","#15A6C1",
                 "#5EB17F","#86B833","#C5D220","#9FC228","#78B113","#49A01D",
                 "#595A5C","#A2A3A4", "#D7D8D7", "#ECECEC", "#FFFFFF", "#000000")

uithof_color_legend = c("#FBB820", "#F59D10", "#E55738", "#DB003F", "#E35493",
                        "#D5267B", "#CC0071", "#A8448A", "#9A3480", "#8D5B9A",
                        "#705296", "#686AA9", "#6173AD", "#4C81BF", "#2F8BC9",
                        "#1290D9", "#1396D8", "#15A6C1", "#5EB17F", "#86B833",
                        "#C5D220", "#9FC228", "#78B113", "#49A01D", "#595A5C",
                        "#A2A3A4", "#D7D8D7", "#ECECEC", "#FFFFFF", "#000000")

#ggplot2 default color palette
gg_color_hue <- function(n) {
  hues = seq(15, 375, length = n + 1)
  hcl(h = hues, l = 65, c = 100)[1:n]
}

### ----------------------------------------------------------------------------
```


```{r Analysis Functions}
# Function to grep data from glm()/lm()
GLM.CON <- function(fit, DATASET, x_name, y, verbose=c(TRUE,FALSE)){
  cat("Analyzing in dataset '", DATASET ,"' the association of '", x_name ,"' with '", y ,"' .\n")
  if (nrow(summary(fit)$coefficients) == 1) {
    output = c(DATASET, x_name, y, rep(NA,8))
    cat("Model not fitted; probably singular.\n")
  }else {
    cat("Collecting data.\n\n")
    effectsize = summary(fit)$coefficients[2,1]
    SE = summary(fit)$coefficients[2,2]
    OReffect = exp(summary(fit)$coefficients[2,1])
    CI_low = exp(effectsize - 1.96 * SE)
    CI_up = exp(effectsize + 1.96 * SE)
    tvalue = summary(fit)$coefficients[2,3]
    pvalue = summary(fit)$coefficients[2,4]
    R = summary(fit)$r.squared
    R.adj = summary(fit)$adj.r.squared
    sample_size = nrow(model.frame(fit))
    AE_N = AEDB.CEA.samplesize
    Perc_Miss = 100 - ((sample_size * 100)/AE_N)
    
    output = c(DATASET, x_name, y, effectsize, SE, OReffect, CI_low, CI_up, tvalue, pvalue, R, R.adj, AE_N, sample_size, Perc_Miss)
    
    if (verbose == TRUE) {
    cat("We have collected the following and summarize it in an object:\n")
    cat("Dataset...................:", DATASET, "\n")
    cat("Score/Exposure/biomarker..:", x_name, "\n")
    cat("Trait/outcome.............:", y, "\n")
    cat("Effect size...............:", round(effectsize, 6), "\n")
    cat("Standard error............:", round(SE, 6), "\n")
    cat("Odds ratio (effect size)..:", round(OReffect, 3), "\n")
    cat("Lower 95% CI..............:", round(CI_low, 3), "\n")
    cat("Upper 95% CI..............:", round(CI_up, 3), "\n")
    cat("T-value...................:", round(tvalue, 6), "\n")
    cat("P-value...................:", signif(pvalue, 8), "\n")
    cat("R^2.......................:", round(R, 6), "\n")
    cat("Adjusted r^2..............:", round(R.adj, 6), "\n")
    cat("Sample size of AE DB......:", AE_N, "\n")
    cat("Sample size of model......:", sample_size, "\n")
    cat("Missing data %............:", round(Perc_Miss, 6), "\n")
    } else {
      cat("Collecting data in summary object.\n")
    }
  }
  return(output)
  print(output)
}

GLM.BIN <- function(fit, DATASET, x_name, y, verbose=c(TRUE,FALSE)){
  cat("Analyzing in dataset '", DATASET ,"' the association of '", x_name ,"' with '", y ,"' ...\n")
  if (nrow(summary(fit)$coefficients) == 1) {
    output = c(DATASET, x_name, y, rep(NA,9))
    cat("Model not fitted; probably singular.\n")
  }else {
    cat("Collecting data...\n")
    effectsize = summary(fit)$coefficients[2,1]
    SE = summary(fit)$coefficients[2,2]
    OReffect = exp(summary(fit)$coefficients[2,1])
    CI_low = exp(effectsize - 1.96 * SE)
    CI_up = exp(effectsize + 1.96 * SE)
    zvalue = summary(fit)$coefficients[2,3]
    pvalue = summary(fit)$coefficients[2,4]
    dev <- fit$deviance
    nullDev <- fit$null.deviance
    modelN <- length(fit$fitted.values)
    R.l <- 1 - dev / nullDev
    R.cs <- 1 - exp(-(nullDev - dev) / modelN)
    R.n <- R.cs / (1 - (exp(-nullDev/modelN)))
    sample_size = nrow(model.frame(fit))
    AE_N = AEDB.CEA.samplesize
    Perc_Miss = 100 - ((sample_size * 100)/AE_N)
    
    output = c(DATASET, x_name, y, effectsize, SE, OReffect, CI_low, CI_up, zvalue, pvalue, R.l, R.cs, R.n, AE_N, sample_size, Perc_Miss)
    if (verbose == TRUE) {
    cat("We have collected the following and summarize it in an object:\n")
    cat("Dataset...................:", DATASET, "\n")
    cat("Score/Exposure/biomarker..:", x_name, "\n")
    cat("Trait/outcome.............:", y, "\n")
    cat("Effect size...............:", round(effectsize, 6), "\n")
    cat("Standard error............:", round(SE, 6), "\n")
    cat("Odds ratio (effect size)..:", round(OReffect, 3), "\n")
    cat("Lower 95% CI..............:", round(CI_low, 3), "\n")
    cat("Upper 95% CI..............:", round(CI_up, 3), "\n")
    cat("Z-value...................:", round(zvalue, 6), "\n")
    cat("P-value...................:", signif(pvalue, 8), "\n")
    cat("Hosmer and Lemeshow r^2...:", round(R.l, 6), "\n")
    cat("Cox and Snell r^2.........:", round(R.cs, 6), "\n")
    cat("Nagelkerke's pseudo r^2...:", round(R.n, 6), "\n")
    cat("Sample size of AE DB......:", AE_N, "\n")
    cat("Sample size of model......:", sample_size, "\n")
    cat("Missing data %............:", round(Perc_Miss, 6), "\n")
    } else {
      cat("Collecting data in summary object.\n")
    }
  }
  return(output)
  print(output)
}
```


# Background

Using a Mendelian Randomization approach, we recently examined associations between the circulating levels of 41 cytokines and growth factors and the risk of stroke in the MEGASTROKE GWAS dataset (67,000 stroke cases and 450,000 controls) and found Monocyte chemoattractant protein-1 (MCP-1) as the cytokine showing the strongest association with stroke, particularly large artery and cardioembolic stroke (Georgakis et al., 2019a). Genetically elevated MCP-1 levels were also associated with a higher risk of coronary artery disease and myocardial infarction (Georgakis et al., 2019a). Further, in a meta-analysis of 6 observational population-based of longitudinal cohort studies we recently showed that baseline levels of MCP-1 were associated with a higher risk of ischemic stroke over follow-up (Georgakis et al., 2019b).
While these data suggest a central role of MCP-1 in the pathogenesis of atherosclerosis, it remains unknown if MCP-1 levels in the blood really reflect MCP-1 activity. MCP-1 is expressed in the atherosclerotic plaque and attracts monocytes in the subendothelial space (Nelken et al., 1991; Papadopoulou et al., 2008; Takeya et al., 1993; Wilcox et al., 1994). Thus, MCP-1 levels in the plaque might more strongly reflect MCP-1 signaling. However, it remains unknown if MCP-1 plaque levels associate with plaque vulnerability or risk of cardiovascular events.


## Objectives

Against this background we now aim to make use of the data from Athero-Express Biobank Study to explore the associations of MCP-1 protein levels in the atherosclerotic plaques from patients undergoing carotid endarterectomy with phenotypes of plaque vulnerability and secondary vascular events over a follow-up of three years.


## Methods

*Blood*

- IL6: Interleukin 6. Entrez Gene: 3569. Bender MedSystems; cat.nr.: BMS810FF. Recalculated FACS. [pg/mL]
- MCP1: Monocyte chemotactic protein 1, MCP-1 (Chemokine (C-C motif) ligand 2, CCL2). Entrez Gene: 6347. Measured at the WKZ. Recalculated Luminex. [pg/mL]


*Plaque*

- IL6: Interleuking 6 (IL6; Entrez Gene: 3569) concentration in plaque [pg/ug], measured by Luminex at the WKZ.
- MCP1: Monocyte chemotactic protein 1 (a.k.a. CCL2; Entrez Gene: 6347) concentration in plaque [pg/ug], measured by Luminex at the WKZ.
- IL6R: Interleuking 6 receptor (IL6R; Entrez Gene: 3570) concentration in plaque [pg/ug], measured by Luminex at the WKZ.


# Loading data

## Clinical data

Loading Athero-Express clinical data.
```{r LoadAEDB}
require(haven)

AEDB <- haven::read_sav(paste0(AEDB_loc, "/2019-3NEW_AtheroExpressDatabase_ScientificAE_02072019_IC_added.sav"))

head(AEDB)

require(openxlsx)
AEDB_ProtConc <- openxlsx::read.xlsx(paste0(AEDB_loc, "/_Vriezer/20190919_Freezers_preTCBio.xlsx"), 
                                     sheet = "ProteinConc.", 
                                     skipEmptyCols = TRUE)

AEDB_Blood <- openxlsx::read.xlsx(paste0(AEDB_loc, "/_Vriezer/20190919_Freezers_preTCBio.xlsx"), 
                                     sheet = "Blood", 
                                     skipEmptyCols = TRUE)
AEDB_Plaque <- openxlsx::read.xlsx(paste0(AEDB_loc, "/_Vriezer/20190919_Freezers_preTCBio.xlsx"), 
                                     sheet = "Plaque", 
                                     skipEmptyCols = TRUE)

head(AEDB_ProtConc)
head(AEDB_Blood)
head(AEDB_Plaque)


```

## Fixing and creating variables

We need to be very strict in defining _symptoms._ Therefore we will fix a new variable that groups _symptoms_ at inclusion.

Coding of _symptoms_ is as follows:

- missing	-999	
- Asymptomatic	0	
- TIA	1	
- minor stroke	2	
- Major stroke	3	
- Amaurosis fugax	4	
- Four vessel disease	5	
- Vertebrobasilary TIA	7	
- Retinal infarction	8	
- Symptomatic, but aspecific symtoms	9
- Contralateral symptomatic occlusion	10	
- retinal infarction	11	
- armclaudication due to occlusion subclavian artery, CEA needed for bypass	12	
- retinal infarction + TIAs	13	
- Ocular ischemic syndrome	14	
- ischemisch glaucoom	15	
- subclavian steal syndrome	16	
- TGA	17

We will group as follows in `Symptoms.5G`:

1. Asymptomatic > 0
2. TIA > 1, 7, 13
3. Stroke > 2, 3
4. Ocular > 4, 14, 15
5. Retinal infarction > 8, 11
6. Other > 5, 9, 10, 12, 16, 17

We will also group as follows in `AsymptSympt`:

1. Asymptomatic > 0
2. TIA > 1, 7, 13 + Stroke > 2, 3
3. Ocular > 4, 14, 15 + Retinal infarction > 8, 11 + Other > 5, 9, 10, 12, 16, 17

```{r FixSymptoms}
# Fix symptoms
attach(AEDB)
# Symptoms.5G
AEDB[,"Symptoms.5G"] <- NA
AEDB$Symptoms.5G[sympt == 0] <- "Asymptomatic"
AEDB$Symptoms.5G[sympt == 1 | sympt == 7 | sympt == 13] <- "TIA"
AEDB$Symptoms.5G[sympt == 2 | sympt == 3] <- "Stroke"
AEDB$Symptoms.5G[sympt == 4 | sympt == 14 | sympt == 15 ] <- "Ocular"
AEDB$Symptoms.5G[sympt == 8 | sympt == 11] <- "Retinal infarction"
AEDB$Symptoms.5G[sympt == 5 | sympt == 9 | sympt == 10 | sympt == 12 | sympt == 16 | sympt == 17] <- "Other"

# AsymptSympt
AEDB[,"AsymptSympt"] <- NA
AEDB$AsymptSympt[sympt == 0] <- "Asymptomatic"
AEDB$AsymptSympt[sympt == 1 | sympt == 7 | sympt == 13 | sympt == 2 | sympt == 3] <- "Symptomatic"
AEDB$AsymptSympt[sympt == 4 | sympt == 14 | sympt == 15 | sympt == 8 | sympt == 11 | sympt == 5 | sympt == 9 | sympt == 10 | sympt == 12 | sympt == 16 | sympt == 17] <- "Ocular and others"
detach(AEDB)

AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "sympt", "Symptoms.5G", "AsymptSympt"))
require(labelled)
AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)

DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)

table(AEDB.temp$Symptoms.5G, AEDB.temp$AsymptSympt)

rm(AEDB.temp)
```

We will also fix the _plaquephenotypes_ variable.  

Coding of symptoms is as follows:

- missing	-999	
- not relevant -888
- fibrous	1	
- fibroatheromatous	2	
- atheromatous	3	


```{r FixPlaquePhenotypes}

# Fix plaquephenotypes
attach(AEDB)
AEDB[,"OverallPlaquePhenotype"] <- NA
AEDB$OverallPlaquePhenotype[plaquephenotype == -999] <- NA
AEDB$OverallPlaquePhenotype[plaquephenotype == -999] <- NA
AEDB$OverallPlaquePhenotype[plaquephenotype == 1] <- "fibrous"
AEDB$OverallPlaquePhenotype[plaquephenotype == 2] <- "fibroatheromatous"
AEDB$OverallPlaquePhenotype[plaquephenotype == 3] <- "atheromatous"
detach(AEDB)

AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "plaquephenotype", "OverallPlaquePhenotype"))
require(labelled)
AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)

DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)

rm(AEDB.temp)


```

We will also fix the _diabetes_ status variable.


```{r FixDiabetes}

# Fix diabetes
attach(AEDB)
AEDB[,"DiabetesStatus"] <- NA
AEDB$DiabetesStatus[DM.composite == -999] <- NA
AEDB$DiabetesStatus[DM.composite == 0] <- "Control (no Diabetes Dx/Med)"
AEDB$DiabetesStatus[DM.composite == 1] <- "Diabetes"
detach(AEDB)

AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "DM.composite", "DiabetesStatus"))
require(labelled)
AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
AEDB.temp$DiabetesStatus <- to_factor(AEDB.temp$DiabetesStatus)

DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)

rm(AEDB.temp)


```

# Athero-Express Biobank Study

## Baseline characteristics

```{r Baseline AEDB: creation, include = FALSE}
cat("====================================================================================================\n")
cat("SELECTION THE SHIZZLE\n")

### Artery levels
# AEdata$Artery_summary: 
#           value                                                                                   label
# NOT USE - 0 No artery known (yet), no surgery (patient ill, died, exited study), re-numbered to AAA
# USE - 1                                                                  carotid (left & right)
# USE - 2                                               femoral/iliac (left, right or both sides)
# NOT USE - 3                                               other carotid arteries (common, external)
# NOT USE - 4                                   carotid bypass and injury (left, right or both sides)
# NOT USE - 5                                                         aneurysmata (carotid & femoral)
# NOT USE - 6                                                                                   aorta
# NOT USE - 7                                            other arteries (renal, popliteal, vertebral)
# NOT USE - 8                        femoral bypass, angioseal and injury (left, right or both sides)

### AEdata$informedconsent
#           value                                                                                           label
# NOT USE - -999                                                                                         missing
# NOT USE - 0                                                                                        no, died
# USE - 1                                                                                             yes
# USE - 2                                                             yes, health treatment when possible
# USE - 3                                                                        yes, no health treatment
# USE - 4                                                yes, no health treatment, no commercial business
# NOT USE - 5                                                          yes, no tissue, no commerical business
# NOT USE - 6                      yes, no tissue, no questionnaires, no medical info, no commercial business
# USE - 7                             yes, no questionnaires, no health treatment, no commercial business
# USE - 8                                          yes, no questionnaires, health treatment when possible
# NOT USE - 9                  yes, no tissue, no questionnaires, no health treatment, no commerical business
# USE - 10                               yes, no health treatment, no medical info, no commercial business
# NOT USE - 11 yes, no tissue, no questionnaires, no health treatment, no medical info, no commercial business
# USE - 12                                                     yes, no questionnaires, no health treatment
# NOT USE - 13                                                             yes, no tissue, no health treatment
# NOT USE - 14                                                               yes, no tissue, no questionnaires
# NOT USE - 15                                                  yes, no tissue, health treatment when possible
# NOT USE - 16                                                                                  yes, no tissue
# USE - 17                                                                     yes, no commerical business
# USE - 18                                     yes, health treatment when possible, no commercial business
# USE - 19                                                    yes, no medical info, no commercial business
# USE - 20                                                                          yes, no questionnaires
# NOT USE - 21                         yes, no tissue, no questionnaires, no health treatment, no medical info
# NOT USE - 22                  yes, no tissue, no questionnaires, no health treatment, no commercial business
# USE - 23                                                                            yes, no medical info
# USE - 24                                                  yes, no questionnaires, no commercial business
# USE - 25                                    yes, no questionnaires, no health treatment, no medical info
# USE - 26                  yes, no questionnaires, health treatment when possible, no commercial business
# USE - 27                                                      yes,  no health treatment, no medical info
# NOT USE - 28                                                                             no, doesn't want to
# NOT USE - 29                                                                              no, unable to sign
# NOT USE - 30                                                                                 no, no reaction
# NOT USE - 31                                                                                        no, lost
# NOT USE - 32                                                                                     no, too old
# NOT USE - 34                                            yes, no medical info, health treatment when possible
# NOT USE - 35                                             no (never asked for IC because there was no tissue)
# USE - 36                    yes, no medical info, no commercial business, health treatment when possible
# NOT USE - 37                                                                                    no, endpoint
# USE - 38                                                         wil niets invullen, wel alles gebruiken
# USE - 39                                           second informed concents: yes, no commercial business
# NOT USE - 40                                                                              nooit geincludeerd

cat("- sanity checking PRIOR to selection")
library(data.table)
ae.gender <- ifelse(AEDB$Gender == 0, "Female", "Male")
ae.hospital <- ifelse(AEDB$Hospital == 1, "Antonius", "UMCU")
table(ae.gender, ae.hospital, dnn = c("Sex", "Hospital"))
ae.gender <- ifelse(AEDB$Gender == 0, "Female", "Male")
table(ae.gender, AEDB$Artery_summary, dnn = c("Sex", "Artery"))
table(ae.gender, AEDB$informedconsent, dnn = c("Sex", "IC"))

rm(ae.gender, ae.hospital)

# I change numeric and factors manually because, well, I wouldn't know how to fix it otherwise
# to have this 'tibble' work with 'tableone'... :-)

AEDB$Age <- as.numeric(AEDB$Age)
AEDB$diastoli <- as.numeric(AEDB$diastoli)
AEDB$systolic <- as.numeric(AEDB$systolic)
AEDB$Age <- as.numeric(AEDB$Age)
AEDB$GFR_MDRD <- as.numeric(AEDB$GFR_MDRD)
AEDB$BMI <- as.numeric(AEDB$BMI)
AEDB$eCigarettes <- as.numeric(AEDB$eCigarettes)
AEDB$ePackYearsSmoking <- as.numeric(AEDB$ePackYearsSmoking)
AEDB$EP_composite_time <- as.numeric(AEDB$EP_composite_time)
AEDB$macmean0 <- as.numeric(AEDB$macmean0)
AEDB$smcmean0 <- as.numeric(AEDB$smcmean0)
AEDB$neutrophils <- as.numeric(AEDB$neutrophils)
AEDB$Mast_cells_plaque <- as.numeric(AEDB$Mast_cells_plaque)
AEDB$vessel_density_averaged <- as.numeric(AEDB$vessel_density_averaged)
AEDB$IL6 <- as.numeric(AEDB$IL6)
AEDB$IL6_pg_ug_2015 <- as.numeric(AEDB$IL6_pg_ug_2015)
AEDB$IL6R_pg_ug_2015 <- as.numeric(AEDB$IL6R_pg_ug_2015)
AEDB$MCP1 <- as.numeric(AEDB$MCP1)
AEDB$MCP1_pg_ug_2015 <- as.numeric(AEDB$MCP1_pg_ug_2015)
AEDB$hsCRP_plasma <- as.numeric(AEDB$hsCRP_plasma)

require(labelled)
AEDB$Gender <- to_factor(AEDB$Gender)
AEDB$Hospital <- to_factor(AEDB$Hospital)
AEDB$KDOQI <- to_factor(AEDB$KDOQI)
AEDB$BMI_WHO <- to_factor(AEDB$BMI_WHO)
AEDB$DiabetesStatus <- to_factor(AEDB$DiabetesStatus)
AEDB$SmokerCurrent <- to_factor(AEDB$SmokerCurrent)
AEDB$Hypertension.composite <- to_factor(AEDB$Hypertension.composite)
AEDB$Hypertension.drugs <- to_factor(AEDB$Hypertension.drugs)
AEDB$Med.anticoagulants <- to_factor(AEDB$Med.anticoagulants)
AEDB$Med.all.antiplatelet <- to_factor(AEDB$Med.all.antiplatelet)
AEDB$Med.Statin.LLD <- to_factor(AEDB$Med.Statin.LLD)
AEDB$Stroke_Dx <- to_factor(AEDB$Stroke_Dx)
AEDB$sympt <- to_factor(AEDB$sympt)
AEDB$Symptoms.3g <- to_factor(AEDB$Symptoms.3g)
AEDB$Symptoms.4g <- to_factor(AEDB$Symptoms.4g)
AEDB$Symptoms.5G <- to_factor(AEDB$Symptoms.5G)
AEDB$AsymptSympt <- to_factor(AEDB$AsymptSympt)

AEDB$restenos <- to_factor(AEDB$restenos)
AEDB$stenose <- to_factor(AEDB$stenose)
AEDB$EP_composite <- to_factor(AEDB$EP_composite)
AEDB$Macrophages.bin <- to_factor(AEDB$Macrophages.bin)
AEDB$SMC.bin <- to_factor(AEDB$SMC.bin)
AEDB$IPH.bin <- to_factor(AEDB$IPH.bin)
AEDB$Calc.bin <- to_factor(AEDB$Calc.bin)
AEDB$Collagen.bin <- to_factor(AEDB$Collagen.bin)
AEDB$Fat.bin_10 <- to_factor(AEDB$Fat.bin_10)
AEDB$Fat.bin_40 <- to_factor(AEDB$Fat.bin_40)
AEDB$OverallPlaquePhenotype <- to_factor(AEDB$OverallPlaquePhenotype)
AEDB$informedconsent <- to_factor(AEDB$informedconsent)
AEDB$Artery_summary <- to_factor(AEDB$Artery_summary)

AEDB.CEA <- subset(AEDB,
                    (Artery_summary == "carotid (left & right)" | Artery_summary == "other carotid arteries (common, external)") & # we only want carotids
                       informedconsent != "missing" & # we are really strict in selecting based on 'informed consent'!
                       informedconsent != "no, died" &
                       informedconsent != "yes, no tissue, no commerical business" &
                       informedconsent != "yes, no tissue, no questionnaires, no medical info, no commercial business" &
                       informedconsent != "yes, no tissue, no questionnaires, no health treatment, no commerical business" &
                       informedconsent != "yes, no tissue, no questionnaires, no health treatment, no medical info, no commercial business" &
                       informedconsent != "yes, no tissue, no health treatment" &
                       informedconsent != "yes, no tissue, no questionnaires" &
                       informedconsent != "yes, no tissue, health treatment when possible" &
                       informedconsent != "yes, no tissue" &
                       informedconsent != "yes, no tissue, no questionnaires, no health treatment, no medical info" &
                       informedconsent != "yes, no tissue, no questionnaires, no health treatment, no commercial business" &
                       informedconsent != "no, doesn't want to" &
                       informedconsent != "no, unable to sign" &
                       informedconsent != "no, no reaction" &
                       informedconsent != "no, lost" &
                       informedconsent != "no, too old" &
                       informedconsent != "yes, no medical info, health treatment when possible" &
                       informedconsent != "no (never asked for IC because there was no tissue)" &
                       informedconsent != "no, endpoint" &
                       informedconsent != "nooit geincludeerd")
AEDB.CEA[1:10, 1:10]
dim(AEDB.CEA)

cat("===========================================================================================\n")
cat("CREATE BASELINE TABLE\n")

# Baseline table variables
basetable_vars = c("Hospital", 
                   "Age", "Gender", 
                   "TC_final", "LDL_final", "HDL_final", "TG_final", 
                   "hsCRP_plasma",
                   "systolic", "diastoli", "GFR_MDRD", "BMI", 
                   "KDOQI", "BMI_WHO",
                   "SmokerCurrent", "eCigarettes", "ePackYearsSmoking",
                   "DiabetesStatus", "Hypertension.composite", 
                   "Hypertension.drugs", "Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD", 
                   "Stroke_Dx", "sympt", "Symptoms.5G", "AsymptSympt",
                   "restenos", "stenose",
                   "EP_composite", "EP_composite_time",
                   "macmean0", "smcmean0", "Macrophages.bin", "SMC.bin",
                   "neutrophils", "Mast_cells_plaque",
                   "IPH.bin", "vessel_density_averaged",
                   "Calc.bin", "Collagen.bin", 
                   "Fat.bin_10", "Fat.bin_40", "OverallPlaquePhenotype",
                   "IL6_pg_ug_2015", "MCP1_pg_ug_2015")

basetable_bin = c("Gender", 
                  "KDOQI", "BMI_WHO",
                  "SmokerCurrent", 
                  "DiabetesStatus", "Hypertension.composite", 
                  "Hypertension.drugs", "Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD", 
                  "Stroke_Dx", "sympt", "Symptoms.5G", "AsymptSympt",
                  "restenos", "stenose",
                  "EP_composite", "Macrophages.bin", "SMC.bin",
                  "IPH.bin", 
                  "Calc.bin", "Collagen.bin", 
                  "Fat.bin_10", "Fat.bin_40", "OverallPlaquePhenotype")
# basetable_bin

basetable_con = basetable_vars[!basetable_vars %in% basetable_bin]
# basetable_con
```

Showing the baseline table of the whole Athero-Express Biobank.
```{r Baseline AEDB: Visualize}
# Create baseline tables
# http://rstudio-pubs-static.s3.amazonaws.com/13321_da314633db924dc78986a850813a50d5.html
AEDB.CEA.tableOne = print(CreateTableOne(vars = basetable_vars, 
                                         # factorVars = basetable_bin,
                                         # strata = "Symptoms.4g",
                                         data = AEDB.CEA, includeNA = TRUE), 
                          nonnormal = c(), missing = TRUE,
                          quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE, 
                          format = "pf", 
                          contDigits = 3)[,1:2]

AEDB.CEA.subset <- subset(AEDB.CEA, !is.na(IL6_pg_ug_2015) | !is.na(MCP1_pg_ug_2015))

AEDB.CEA.subset.tableOne = print(CreateTableOne(vars = basetable_vars, 
                                         # factorVars = basetable_bin,
                                         # strata = "DiabetesStatus",
                                         data = AEDB.CEA.subset, includeNA = TRUE), 
                          nonnormal = c(), missing = TRUE,
                          quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE, 
                          format = "pf", 
                          contDigits = 3)[,1:2]

AEDB.CEA.subset.serum <- subset(AEDB.CEA, !is.na(IL6) | !is.na(MCP1))

AEDB.CEA.subset.serum.tableOne = print(CreateTableOne(vars = basetable_vars, 
                                         # factorVars = basetable_bin,
                                         # strata = "DiabetesStatus",
                                         data = AEDB.CEA.subset.serum, includeNA = TRUE), 
                          nonnormal = c(), missing = TRUE,
                          quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE, 
                          format = "pf", 
                          contDigits = 3)[,1:2]
```

Writing the baseline table to Excel format. 
```{r Baseline AEDB: write}
# Write basetable
require(openxlsx)

write.xlsx(file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AE.BaselineTable.wholeCEA.xlsx"),
           AEDB.CEA.tableOne, 
           row.names = TRUE, 
           col.names = TRUE, 
           sheetName = "wholeAEDB_Baseline")

write.xlsx(file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AE.BaselineTable.subsetCEA.xlsx"),
           AEDB.CEA.subset.tableOne, 
           row.names = TRUE, 
           col.names = TRUE, 
           sheetName = "subsetAEDB_Baseline")

write.xlsx(file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AE.BaselineTable.subsetCEAserum.xlsx"),
           AEDB.CEA.subset.serum.tableOne, 
           row.names = TRUE, 
           col.names = TRUE, 
           sheetName = "subsetAEDB_Baseline_serum")
```

# Athero-Express Genomics Study (AEGS)

We will add the samples which were genotyped in the Athero-Express Biobank Study, _i.e._ Athero-Express Genomics Study 1 (AEGS1, Affymetrix SNP 5.0), AEGS2 (Affymetrix Axiom CEU), and AEGS3 (Illumina GSA).

## Loading AEGS data 

```{r create AEGS}
AEGS1_2_3 <- fread(paste0(ROOT_loc,"/PLINK/_AE_Originals/AEGS_COMBINED_QC2018/aegs1_2_3_combo_postqcmichimp_n2493.forR.txt"),
                   verbose = TRUE, showProgress = TRUE)
AEGS1_2_3$Study_Number <- as.numeric(AEGS1_2_3$Study_Number)
AEGS1_2_3$Age <- NULL
dim(AEGS1_2_3)
head(AEGS1_2_3)

AEGS_raw <- merge(AEGS1_2_3, AEDB, by.x = "Study_Number", by.y = "STUDY_NUMBER", sort = FALSE,
                  all.x = TRUE)

dim(AEGS_raw)
warnings() 

```


Here we will subset only those genotyped samples that passed genotyping quality control, are unrelated, and have informed consent.
```{r SpecificSelection}
AEGS_raw$Artery_summary <- to_factor(AEGS_raw$Artery_summary)
AEGS_raw$informedconsent <- to_factor(AEGS_raw$informedconsent)
table(AEGS_raw$Artery_summary, AEGS_raw$QC2018_FILTER)
table(AEGS_raw$informedconsent, AEGS_raw$QC2018_FILTER)
AEGSselect <- subset(AEGS_raw, 
                     QC2018_FILTER != "issue" & QC2018_FILTER != "family_discard" &
                       (Artery_summary == "carotid (left & right)" | Artery_summary == "other carotid arteries (common, external)") & 
                       informedconsent != "missing" &
                       informedconsent != "no, died" &
                       informedconsent != "yes, no tissue, no commerical business" &
                       informedconsent != "yes, no tissue, no questionnaires, no medical info, no commercial business" &
                       informedconsent != "yes, no tissue, no questionnaires, no health treatment, no commerical business" &
                       informedconsent != "yes, no tissue, no questionnaires, no health treatment, no medical info, no commercial business" &
                       informedconsent != "yes, no tissue, no health treatment" &
                       informedconsent != "yes, no tissue, no questionnaires" &
                       informedconsent != "yes, no tissue, health treatment when possible" &
                       informedconsent != "yes, no tissue" &
                       informedconsent != "yes, no tissue, no questionnaires, no health treatment, no medical info" &
                       informedconsent != "yes, no tissue, no questionnaires, no health treatment, no commercial business" &
                       informedconsent != "no, doesn't want to" &
                       informedconsent != "no, unable to sign" &
                       informedconsent != "no, no reaction" &
                       informedconsent != "no, lost" &
                       informedconsent != "no, too old" &
                       informedconsent != "yes, no medical info, health treatment when possible" &
                       informedconsent != "no (never asked for IC because there was no tissue)" &
                       informedconsent != "no, endpoint" &
                       informedconsent != "nooit geincludeerd")
dim(AEGSselect)

table(AEGSselect$Artery_summary, AEGSselect$QC2018_FILTER)
table(AEGSselect$Artery_summary, AEGSselect$CHIP)
table(AEGSselect$QC2018_FILTER, AEGSselect$CHIP)
table(AEGSselect$QC2018_FILTER, AEGSselect$SAMPLE_TYPE)

AEDB.temp <- subset(AEGSselect,  select = c("Study_Number", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "QC2018_FILTER", "CHIP", "SAMPLE_TYPE"))
require(labelled)
AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
AEDB.temp$QC2018_FILTER <- to_factor(AEDB.temp$QC2018_FILTER)
AEDB.temp$CHIP <- to_factor(AEDB.temp$CHIP)
AEDB.temp$SAMPLE_TYPE <- to_factor(AEDB.temp$SAMPLE_TYPE)

DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)

rm(AEDB.temp)

```

## Baseline characteristics
Showing the baseline table of the whole Athero-Express Genomics Study.
```{r Baseline SampleSelect: Visualize}
# Create baseline tables
# http://rstudio-pubs-static.s3.amazonaws.com/13321_da314633db924dc78986a850813a50d5.html

basetable_vars_geno = c("Hospital", 
                   "Age", "Gender", 
                   "TC_final", "LDL_final", "HDL_final", "TG_final", 
                   "hsCRP_plasma",
                   "systolic", "diastoli", "GFR_MDRD", "BMI", 
                   "KDOQI", "BMI_WHO",
                   "SmokerCurrent", "eCigarettes", "ePackYearsSmoking",
                   "DiabetesStatus", "Hypertension.composite", 
                   "Hypertension.drugs", "Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD", 
                   "Stroke_Dx", "sympt", "Symptoms.5G", "AsymptSympt",
                   "restenos", "stenose",
                   "EP_composite", "EP_composite_time",
                   "macmean0", "smcmean0", "Macrophages.bin", "SMC.bin",
                   "neutrophils", "Mast_cells_plaque",
                   "IPH.bin", "vessel_density_averaged",
                   "Calc.bin", "Collagen.bin", 
                   "Fat.bin_10", "Fat.bin_40", "OverallPlaquePhenotype",
                   "IL6_pg_ug_2015", "MCP1_pg_ug_2015", 
                   "QC2018_FILTER", "CHIP", "SAMPLE_TYPE")

basetable_bin_geno = c("Gender", 
                  "KDOQI", "BMI_WHO",
                  "SmokerCurrent", 
                  "DiabetesStatus", "Hypertension.composite", 
                  "Hypertension.drugs", "Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD", 
                  "Stroke_Dx", "sympt", "Symptoms.5G", "AsymptSympt",
                  "restenos", "stenose",
                  "EP_composite", "Macrophages.bin", "SMC.bin",
                  "IPH.bin", 
                  "Calc.bin", "Collagen.bin", 
                  "Fat.bin_10", "Fat.bin_40", "OverallPlaquePhenotype",
                  "QC2018_FILTER", "CHIP", "SAMPLE_TYPE")

basetable_con_geno = basetable_vars_geno[!basetable_vars_geno %in% basetable_bin_geno]

AEGSselect.tableOne = print(CreateTableOne(vars = basetable_vars_geno, 
                                         # factorVars = basetable_bin,
                                         # strata = "DiabetesStatus",
                                         data = AEGSselect, includeNA = TRUE), 
                          nonnormal = c(), missing = TRUE,
                          quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE, 
                          format = "pf", 
                          contDigits = 3)[,1:2]

AEGSselect.subset <- subset(AEGSselect, !is.na(IL6R_pg_ug_2015) | !is.na(MCP1_pg_ug_2015))

AEGSselect.subset.tableOne = print(CreateTableOne(vars = basetable_vars_geno, 
                                         # factorVars = basetable_bin,
                                         # strata = "DiabetesStatus",
                                         data = AEGSselect.subset, includeNA = TRUE), 
                          nonnormal = c(), missing = TRUE,
                          quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE, 
                          format = "pf", 
                          contDigits = 3)[,1:2]

```

Let's also save these baseline tables.
```{r Baseline SampleSelection: write}
# Write basetable
require(openxlsx)

write.xlsx(file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AEGS.BaselineTable.xlsx"), 
           AEGSselect.tableOne, 
           row.names = TRUE, 
           col.names = TRUE, 
           sheetName = "AEGS_Baseline")

write.xlsx(file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AEGS.BaselineTable.subset.xlsx"), 
           AEGSselect.subset.tableOne, 
           row.names = TRUE, 
           col.names = TRUE, 
           sheetName = "AEGS_Baseline_subset")

```


## Data exploration

Here we inspect the data and when necessary transform quantitative measures. We will inspect the raw, natural log transformed + the smallest measurement, and inverse-normal transformation. 

```{r DataExploration: IL6 plaque}

summary(AEDB.CEA$IL6_pg_ug_2015)

ggpubr::gghistogram(AEDB.CEA, "IL6_pg_ug_2015", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "mean", 
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2), 
                    title = "IL6 plaque levels",
                    xlab = "pg/ug", 
                    ggtheme = theme_minimal())

min_IL6pg_ug_2015 <- min(AEDB.CEA$IL6_pg_ug_2015, na.rm = TRUE)
min_IL6pg_ug_2015

AEDB.CEA$IL6_pg_ug_2015_LN <- log(AEDB.CEA$IL6_pg_ug_2015 + min_IL6pg_ug_2015)

ggpubr::gghistogram(AEDB.CEA, "IL6_pg_ug_2015_LN", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "mean", 
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2), 
                    title = "IL6 plaque levels",
                    xlab = "natural log-transformed pg/ug", 
                    ggtheme = theme_minimal())

AEDB.CEA$IL6_pg_ug_2015_rank <- qnorm((rank(AEDB.CEA$IL6_pg_ug_2015, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$IL6_pg_ug_2015)))
ggpubr::gghistogram(AEDB.CEA, "IL6_pg_ug_2015_rank", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "mean", 
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2), 
                    title = "IL6 plaque levels",
                    xlab = "inverse-normal transformation pg/ug", 
                    ggtheme = theme_minimal())
```

```{r DataExploration: IL6R plaque}

summary(AEDB.CEA$IL6R_pg_ug_2015)

ggpubr::gghistogram(AEDB.CEA, "IL6R_pg_ug_2015", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "mean", 
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2), 
                    title = "IL6R plaque levels",
                    xlab = "pg/ug", 
                    ggtheme = theme_minimal())

min_IL6R_pg_ug_2015 <- min(AEDB.CEA$IL6R_pg_ug_2015, na.rm = TRUE)
min_IL6R_pg_ug_2015

AEDB.CEA$IL6R_pg_ug_2015_LN <- log(AEDB.CEA$IL6R_pg_ug_2015 + min_IL6R_pg_ug_2015)
ggpubr::gghistogram(AEDB.CEA, "IL6R_pg_ug_2015_LN", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "mean", 
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2), 
                    title = "IL6R plaque levels",
                    xlab = "natural log-transformed pg/ug", 
                    ggtheme = theme_minimal())

AEDB.CEA$IL6R_pg_ug_2015_rank <- qnorm((rank(AEDB.CEA$IL6R_pg_ug_2015, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$IL6R_pg_ug_2015)))
ggpubr::gghistogram(AEDB.CEA, "IL6R_pg_ug_2015_rank", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "mean", 
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2), 
                    title = "IL6R plaque levels",
                    xlab = "inverse-normal transformation pg/ug", 
                    ggtheme = theme_minimal())
```

```{r DataExploration: MCP1 plaque}

summary(AEDB.CEA$MCP1_pg_ug_2015)

ggpubr::gghistogram(AEDB.CEA, "MCP1_pg_ug_2015", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "mean", 
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2), 
                    title = "MCP1 plaque levels",
                    xlab = "pg/ug", 
                    ggtheme = theme_minimal())

min_MCP1_pg_ug_2015 <- min(AEDB.CEA$MCP1_pg_ug_2015, na.rm = TRUE)
min_MCP1_pg_ug_2015

AEDB.CEA$MCP1_pg_ug_2015_LN <- log(AEDB.CEA$MCP1_pg_ug_2015 + min_MCP1_pg_ug_2015)
ggpubr::gghistogram(AEDB.CEA, "MCP1_pg_ug_2015_LN", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "mean", 
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2), 
                    title = "MCP1 plaque levels",
                    xlab = "natural log-transformed pg/ug", 
                    ggtheme = theme_minimal())

AEDB.CEA$MCP1_pg_ug_2015_rank <- qnorm((rank(AEDB.CEA$MCP1_pg_ug_2015, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$MCP1_pg_ug_2015)))
ggpubr::gghistogram(AEDB.CEA, "MCP1_pg_ug_2015_rank", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "mean", 
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2), 
                    title = "MCP1 plaque levels",
                    xlab = "inverse-normal transformation pg/ug", 
                    ggtheme = theme_minimal())
```

```{r DataExploration: IL6 serum}

summary(AEDB.CEA$IL6)

ggpubr::gghistogram(AEDB.CEA, "IL6", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "mean", 
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2), 
                    title = "IL6 serum levels",
                    xlab = "pg/mL", 
                    ggtheme = theme_minimal())

min_IL6 <- min(AEDB.CEA$IL6, na.rm = TRUE)
min_IL6

AEDB.CEA$IL6_LN <- log(AEDB.CEA$IL6 + min_IL6)
ggpubr::gghistogram(AEDB.CEA, "IL6_LN", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "mean", 
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2), 
                    title = "IL6 serum levels",
                    xlab = "natural log-transformed pg/ug", 
                    ggtheme = theme_minimal())

AEDB.CEA$IL6_rank <- qnorm((rank(AEDB.CEA$IL6, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$IL6)))
ggpubr::gghistogram(AEDB.CEA, "IL6_rank", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "mean", 
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2), 
                    title = "IL6 serum levels",
                    xlab = "inverse-normal transformation pg/ug", 
                    ggtheme = theme_minimal())
```

```{r DataExploration: MCP1 serum}

summary(AEDB.CEA$MCP1)

ggpubr::gghistogram(AEDB.CEA, "MCP1", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "mean", 
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2), 
                    title = "MCP1 serum levels",
                    xlab = "pg/mL", 
                    ggtheme = theme_minimal())

min_MCP1 <- min(AEDB.CEA$MCP1, na.rm = TRUE)
min_MCP1

AEDB.CEA$MCP1_LN <- log(AEDB.CEA$MCP1 + min_MCP1)
ggpubr::gghistogram(AEDB.CEA, "MCP1_LN", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "mean", 
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2), 
                    title = "MCP1 serum levels",
                    xlab = "natural log-transformed pg/ug", 
                    ggtheme = theme_minimal())

AEDB.CEA$MCP1_rank <- qnorm((rank(AEDB.CEA$MCP1, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$MCP1)))
ggpubr::gghistogram(AEDB.CEA, "MCP1_rank", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "mean", 
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2), 
                    title = "MCP1 serum levels",
                    xlab = "inverse-normal transformation pg/ug", 
                    ggtheme = theme_minimal())
```

## Preliminary conclusion data exploration

After discussion we decided to pursuit the following strategy. In line with the previous work by [Marios Georgakis](https://www.ahajournals.org/doi/full/10.1161/CIRCRESAHA.119.315380){target="_blank"} we will apply _natural log transformation_ on all proteins and focus the analysis on:

- MCP1 (serum and plaque), 
- IL6 (serum and plaque), and 
- IL6R (in plaque).

# Analyses

The analyses are focused on three elements: 

1) plaque vulnerability phenotypes
2) clinical status at inclusion (symptoms)
3) secondary clinical outcome during three (3) years of follow-up

## Covariates & other variables

1.  Age (continuous in 1-year increment). [Age]
2.  Sex (male vs. female). [Gender]
3.  Presence of hypertension at baseline (defined either as history of hypertension, SBP ≥140 mm Hg, DBP ≥90 mm Hg, or prescription of antihypertensive medications). [Hypertension.composite]
4.  Presence of diabetes mellitus at baseline (defined either as a history of diabetes, administration of glucose lowering medication, HbA1c ≥6.5%, fasting glucose ≥126 mg/dl, .or random glucose levels ≥200 mg/dl). [DiabetesStatus]
5.  Smoking (current, ex-, never). [SmokerCurrent]
6.  LDL-C levels (continuous). [LDL_final]
7.  Use of lipid-lowering drugs. [Med.Statin.LLD]
8.  Use of antiplatelet drugs. [Med.all.antiplatelet]
9.  eGFR (continuous). [GFR_MDRD]
10.	BMI (continuous). [BMI]
11.	History of cardiovascular disease (stroke, coronary artery disease, peripheral artery disease). [CAD_history, Stroke_history, Peripheral.interv]
12.	Level of stenosis (50-70% vs. 70-99%). [stenose]
13.	Presenting symptoms (asymptomatic, ocular, TIA, or stroke). [Symptoms.5G]
14.	hsCRP circulating levels (ln-transformed, continuous). [hsCRP_plasma]
15.	IL-6 plaque levels (ln-transformed, continuous). [IL6_pg_ug_2015_LN]

## Models

We will analyze the data through four different models

- Model 1: adjusted for age and sex
- Model 2: adjusted for age, sex, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis,
- Model 3: same to model 2, with additional adjustments for circulating CRP levels
- Model 4: same to model 2 with additional adjustment for IL6 levels in the plaque

## A. Cross-sectional analysis plaque phenotypes

In the cross-sectional analysis of plaque and serum MCP1, IL6, and IL6R levels we will focus on the following plaque vulnerability phenotypes:

- Percentage of macrophages (continuous trait)
- Percentage of SMCs (continuous trait)
- Number of intraplaque microvessels per 3-4 hotspots (continuous trait)
- Presence of moderate/heavy calcifications (binary trait)
- Presence of moderate/heavy collagen content (binary trait)
- Presence of lipid core no/<10% vs. >10% (binary trait)
- Presence of intraplaque hemorrhage (binary trait)

*Continous traits*
```{r CrossSec: plaques - transformations and visualisations continuous}

# macrophages
cat("Summary of data.\n")
summary(AEDB.CEA$macmean0)

min_macmean <- min(AEDB.CEA$macmean0, na.rm = TRUE)
cat(paste0("\nMinimum value % macrophages: ",min_macmean,".\n"))

AEDB.CEA$Macrophages_LN <- log(AEDB.CEA$macmean0 + min_macmean)

ggpubr::gghistogram(AEDB.CEA, "Macrophages_LN", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "% macrophages",
                    xlab = "natural log-transformed %", 
                    ggtheme = theme_minimal())

AEDB.CEA$Macrophages_rank <- qnorm((rank(AEDB.CEA$macmean0, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$macmean0)))
ggpubr::gghistogram(AEDB.CEA, "Macrophages_rank", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "% macrophages",
                    xlab = "inverse-rank normalized %", 
                    ggtheme = theme_minimal())

# smooth muscle cells
cat("Summary of data.\n")
summary(AEDB.CEA$macmean0)

min_smcmean <- min(AEDB.CEA$smcmean0, na.rm = TRUE)
cat(paste0("\nMinimum value % smooth muscle cells: ",min_smcmean,".\n"))

AEDB.CEA$SMC_LN <- log(AEDB.CEA$smcmean0 + min_smcmean)

ggpubr::gghistogram(AEDB.CEA, "SMC_LN", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "% smooth muscle cells",
                    xlab = "natural log-transformed %", 
                    ggtheme = theme_minimal())

AEDB.CEA$SMC_rank <- qnorm((rank(AEDB.CEA$smcmean0, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$smcmean0)))
ggpubr::gghistogram(AEDB.CEA, "SMC_rank", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "% smooth muscle cells",
                    xlab = "inverse-rank normalized %", 
                    ggtheme = theme_minimal())

# vessel density
cat("Summary of data.\n")
summary(AEDB.CEA$vessel_density_averaged)

min_vesseldensity <- min(AEDB.CEA$vessel_density_averaged, na.rm = TRUE)
min_vesseldensity
cat(paste0("\nMinimum value number of intraplaque neovessels per 3-4 hotspots: ",min_vesseldensity,".\n"))

AEDB.CEA$VesselDensity_LN <- log(AEDB.CEA$vessel_density_averaged + min_vesseldensity)

ggpubr::gghistogram(AEDB.CEA, "VesselDensity_LN", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "number of intraplaque neovessels per 3-4 hotspots",
                    xlab = "natural log-transformed number", 
                    ggtheme = theme_minimal())

AEDB.CEA$VesselDensity_rank <- qnorm((rank(AEDB.CEA$vessel_density_averaged, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$vessel_density_averaged)))
ggpubr::gghistogram(AEDB.CEA, "VesselDensity_rank", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "number of intraplaque neovessels per 3-4 hotspots",
                   xlab = "inverse-rank normalized number", 
                    ggtheme = theme_minimal())
```

*Binary traits*
```{r CrossSec: plaques - transformations and visualisations binary}

# calcification
cat("Summary of data.\n")
summary(AEDB.CEA$Calc.bin)
contrasts(AEDB.CEA$Calc.bin)

AEDB.CEA$CalcificationPlaque <- as.factor(AEDB.CEA$Calc.bin)

df <- AEDB.CEA %>%
  filter(!is.na(CalcificationPlaque)) %>%
  group_by(Gender, CalcificationPlaque) %>%
summarise(counts = n()) 

ggpubr::ggbarplot(df, x = "CalcificationPlaque", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Calcification",
                    xlab = "calcification", 
                    ggtheme = theme_minimal())
rm(df)

# collagen
cat("Summary of data.\n")
summary(AEDB.CEA$Collagen.bin)
contrasts(AEDB.CEA$Collagen.bin)

AEDB.CEA$CollagenPlaque <- as.factor(AEDB.CEA$Collagen.bin)

df <- AEDB.CEA %>%
  filter(!is.na(CollagenPlaque)) %>%
  group_by(Gender, CollagenPlaque) %>%
summarise(counts = n()) 

ggpubr::ggbarplot(df, x = "CollagenPlaque", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Collagen",
                    xlab = "collagen", 
                    ggtheme = theme_minimal())
rm(df)

# fat 10%
cat("Summary of data.\n")
summary(AEDB.CEA$Fat.bin_10)
contrasts(AEDB.CEA$Fat.bin_10)

AEDB.CEA$Fat10Perc <- as.factor(AEDB.CEA$Fat.bin_10)

df <- AEDB.CEA %>%
  filter(!is.na(Fat10Perc)) %>%
  group_by(Gender, Fat10Perc) %>%
summarise(counts = n()) 

ggpubr::ggbarplot(df, x = "Fat10Perc", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Intraplaque fat",
                    xlab = "intraplaque fat", 
                    ggtheme = theme_minimal())
rm(df)

# IPH
cat("Summary of data.\n")
summary(AEDB.CEA$IPH.bin)
contrasts(AEDB.CEA$IPH.bin)

AEDB.CEA$IPH <- as.factor(AEDB.CEA$IPH.bin)

df <- AEDB.CEA %>%
  filter(!is.na(IPH)) %>%
  group_by(Gender, IPH) %>%
summarise(counts = n()) 

ggpubr::ggbarplot(df, x = "IPH", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Intraplaque hemorrhage",
                    xlab = "intraplaque hemorrhage", 
                    ggtheme = theme_minimal())
rm(df)

# Symptoms
cat("Summary of data.\n")
summary(AEDB.CEA$AsymptSympt)
contrasts(AEDB.CEA$AsymptSympt)

AEDB.CEA$AsymptSympt <- as.factor(AEDB.CEA$AsymptSympt)

df <- AEDB.CEA %>%
  filter(!is.na(AsymptSympt)) %>%
  group_by(Gender, AsymptSympt) %>%
summarise(counts = n()) 

ggpubr::ggbarplot(df, x = "AsymptSympt", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Symptoms",
                    xlab = "symptoms", 
                    ggtheme = theme_minimal())
rm(df)

```

In this section we make some variables to assist with analysis.
```{r CrossSec: plaques - setup regression }
AEDB.CEA.samplesize = nrow(AEDB.CEA)
TRAITS.PROTEIN = c("IL6_LN", "MCP1_LN", "IL6_pg_ug_2015_LN", "IL6R_pg_ug_2015_LN", "MCP1_pg_ug_2015_LN")
TRAITS.PROTEIN.RANK = c("IL6_rank", "MCP1_rank", "IL6_pg_ug_2015_rank", "IL6R_pg_ug_2015_rank", "MCP1_pg_ug_2015_rank")

TRAITS.CON = c("Macrophages_LN", "SMC_LN", "VesselDensity_LN") 
TRAITS.CON.RANK = c("Macrophages_rank", "SMC_rank", "VesselDensity_rank")

TRAITS.BIN = c("CalcificationPlaque", "CollagenPlaque", "Fat10Perc", "IPH")

# "Hospital", 
# "Age", "Gender", 
# "TC_final", "LDL_final", "HDL_final", "TG_final", 
# "systolic", "diastoli", "GFR_MDRD", "BMI", 
# "KDOQI", "BMI_WHO",
# "SmokerCurrent", "eCigarettes", "ePackYearsSmoking",
# "DiabetesStatus", "Hypertension.composite", 
# "Hypertension.drugs", "Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD", 
# "Stroke_Dx", "sympt", "Symptoms.5G", "restenos",
# "EP_composite", "EP_composite_time",
# "macmean0", "smcmean0", "Macrophages.bin", "SMC.bin",
# "neutrophils", "Mast_cells_plaque",
# "IPH.bin", "vessel_density_averaged",
# "Calc.bin", "Collagen.bin", 
# "Fat.bin_10", "Fat.bin_40", "OverallPlaquePhenotype",
# "IL6_pg_ug_2015", "MCP1_pg_ug_2015", 
# "QC2018_FILTER", "CHIP", "SAMPLE_TYPE",
# "CAD_history", "Stroke_history", "Peripheral.interv",
# "stenose"

# 1.  Age (continuous in 1-year increment). [Age]
# 2.  Sex (male vs. female). [Gender]
# 3.  Presence of hypertension at baseline (defined either as history of hypertension, SBP ≥140 mm Hg, DBP ≥90 mm Hg, or prescription of antihypertensive medications). [Hypertension.composite]
# 4.  Presence of diabetes mellitus at baseline (defined either as a history of diabetes, administration of glucose lowering medication, HbA1c ≥6.5%, fasting glucose ≥126 mg/dl, .or random glucose levels ≥200 mg/dl). [DiabetesStatus]
# 5.  Smoking (current, ex-, never). [SmokerCurrent]
# 6.  LDL-C levels (continuous). [LDL_final]
# 7.  Use of lipid-lowering drugs. [Med.Statin.LLD]
# 8.  Use of antiplatelet drugs. [Med.all.antiplatelet]
# 9.  eGFR (continuous). [GFR_MDRD]
# 10.	BMI (continuous). [BMI]
# 11.	History of cardiovascular disease (stroke, coronary artery disease, peripheral artery disease). [CAD_history, Stroke_history, Peripheral.interv]
# 12.	Level of stenosis (50-70% vs. 70-99%). [stenose]
# 13.	Presenting symptoms (asymptomatic, ocular, TIA, or stroke). [Symptoms.5G]
# 14.	hsCRP circulating levels (ln-transformed, continuous). [hsCRP_plasma]
# 15.	IL-6 plaque levels (ln-transformed, continuous). [IL6_pg_ug_2015_LN]

# Models 
# Model 1: adjusted for age and sex
# Model 2: adjusted for age, sex, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis,
# Model 3: same to model 2, with additional adjustments for circulating CRP levels
# Model 4: same to model 2 with additional adjustment for IL6 levels in the plaque

COVARIATES_M1 = c("Age", "Gender")

COVARIATES_M2 = c(COVARIATES_M1,  
               "Hypertension.composite", "DiabetesStatus", "SmokerCurrent",
               "Med.Statin.LLD", "Med.all.antiplatelet", 
               "GFR_MDRD", "BMI", 
               "CAD_history", "Stroke_history", "Peripheral.interv",
               "stenose")

COVARIATES_M3 = c(COVARIATES_M2, "LDL_final")

COVARIATES_M4 = c(COVARIATES_M2, "hsCRP_plasma")

COVARIATES_M5 = c(COVARIATES_M2, "IL6_pg_ug_2015_LN")
COVARIATES_M5rank = c(COVARIATES_M2, "IL6_pg_ug_2015_rank")

```

### Model 1

In this model we correct for _Age_ and _Gender_.


#### Natural log-transformed data

First we use the natural-log transformed data.

Analysis of continuous/quantitative plaque traits as a function of serum/plaque IL6(R)/MCP1 levels.
```{r CrossSec: plaques - linear regression MODEL1, include=TRUE, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")
for (protein in 1:length(TRAITS.PROTEIN)) {
  PROTEIN = TRAITS.PROTEIN[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(TRAITS.CON)) {
    TRAIT = TRAITS.CON[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M1) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    ### univariate
    fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender, data = currentDF)
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
    GLM.results.TEMP[1,] = GLM.CON(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "T-value", "P-value", "r^2", "r^2_adj", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")
### Univariate
library(openxlsx)
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Con.Uni.Protein.PlaquePhenotypes.MODEL1.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Con.Uni.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)


```

Analysis of binary plaque traits as a function of serum/plaque IL6(R)/MCP1 levels.
```{r CrossSec: plaques - logistic regression MODEL1, include=TRUE, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN)) {
  PROTEIN = TRAITS.PROTEIN[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(TRAITS.BIN)) {
    TRAIT = TRAITS.BIN[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M1) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate
    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender,
              data  =  currentDF, family = binomial(link = "logit"))
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")

### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Uni.Protein.PlaquePhenotypes.MODEL1.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Bin.Uni.PlaquePheno")

# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

```

#### Inverse-rank normalization

Here we use the inverse-rank normalized data - visually this is more normally distributed.

Analysis of continuous/quantitative plaque traits as a function of serum/plaque IL6(R)/MCP1 levels.
```{r CrossSec: plaques - linear regression MODEL1 RANK, include=TRUE, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(TRAITS.CON.RANK)) {
    TRAIT = TRAITS.CON.RANK[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M1) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    ### univariate
    fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender, data = currentDF)
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))

    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
    GLM.results.TEMP[1,] = GLM.CON(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "T-value", "P-value", "r^2", "r^2_adj", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")
### Univariate
library(openxlsx)
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Con.Uni.Protein.PlaquePhenotypes.RANK.MODEL1.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Con.Uni.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)


```

Analysis of binary plaque traits as a function of serum/plaque IL6(R)/MCP1 levels.
```{r CrossSec: plaques - logistic regression MODEL1 RANK, include=TRUE, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(TRAITS.BIN)) {
    TRAIT = TRAITS.BIN[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M1) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate
    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender,
              data  =  currentDF, family = binomial(link = "logit"))
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")

### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Uni.Protein.PlaquePhenotypes.RANK.MODEL1.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Bin.Uni.PlaquePheno")

# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

```


### Model 2

In this model we correct for _Age_, _Gender_, _Hypertension status_, _Diabetes status_, _current smoker status_, _lipid-lowering drugs (LLDs)_, _antiplatelet medication_, _eGFR (MDRD)_, _BMI_, _CAD history_, _stroke history_, _peripheral interventions_, and _stenosis_.

#### Natural log-transformed data

First we use the natural-log transformed data.

Analysis of continuous/quantitative plaque traits as a function of serum/plaque IL6(R)/MCP1 levels.
```{r CrossSec: plaques - linear regression MODEL2}

GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")
for (protein in 1:length(TRAITS.PROTEIN)) {
  PROTEIN = TRAITS.PROTEIN[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(TRAITS.CON)) {
    TRAIT = TRAITS.CON[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M2) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    ### univariate
    fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender + 
                Hypertension.composite + DiabetesStatus + SmokerCurrent +
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
                CAD_history + Stroke_history + Peripheral.interv + stenose, 
              data = currentDF)
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
    GLM.results.TEMP[1,] = GLM.CON(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "T-value", "P-value", "r^2", "r^2_adj", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")
### Univariate
library(openxlsx)
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Con.Multi.Protein.PlaquePhenotypes.MODEL2.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Con.Multi.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)


```

Analysis of binary plaque traits as a function of serum/plaque IL6(R)/MCP1 levels.
```{r CrossSec: plaques - logistic regression MODEL2, include=TRUE, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN)) {
  PROTEIN = TRAITS.PROTEIN[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(TRAITS.BIN)) {
    TRAIT = TRAITS.BIN[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M2) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate
    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + 
                Hypertension.composite + DiabetesStatus + SmokerCurrent + 
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
                CAD_history + Stroke_history + Peripheral.interv + stenose, 
              data  =  currentDF, family = binomial(link = "logit"))
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")

### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Multi.Protein.PlaquePhenotypes.MODEL2.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Bin.Multi.PlaquePheno")

# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

```

#### Inverse-rank normalization

Here we use the inverse-rank normalized data - visually this is more normally distributed.

Analysis of continuous/quantitative plaque traits as a function of serum/plaque IL6(R)/MCP1 levels.
```{r CrossSec: plaques - linear regression MODEL2 RANK, include=TRUE, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(TRAITS.CON.RANK)) {
    TRAIT = TRAITS.CON.RANK[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M2) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    ### univariate
    fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender + 
                Hypertension.composite + DiabetesStatus + SmokerCurrent + 
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
                CAD_history + Stroke_history + Peripheral.interv + stenose, 
              data = currentDF)
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
    GLM.results.TEMP[1,] = GLM.CON(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "T-value", "P-value", "r^2", "r^2_adj", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")
### Univariate
library(openxlsx)
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Con.Multi.Protein.PlaquePhenotypes.RANK.MODEL2.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Con.Multi.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)


```

Analysis of binary plaque traits as a function of serum/plaque IL6(R)/MCP1 levels.
```{r CrossSec: plaques - logistic regression MODEL2 RANK, include=TRUE, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(TRAITS.BIN)) {
    TRAIT = TRAITS.BIN[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M2) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate
    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + 
                Hypertension.composite + DiabetesStatus + SmokerCurrent + 
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
                CAD_history + Stroke_history + Peripheral.interv + stenose, 
              data  =  currentDF, family = binomial(link = "logit"))
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")

### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Multi.Protein.PlaquePhenotypes.RANK.MODEL2.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Bin.Multi.PlaquePheno")

# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

```

### Model 3

In this model we correct for _Age_, _Gender_, _Hypertension status_, _Diabetes status_, _current smoker status_, _lipid-lowering drugs (LLDs)_, _antiplatelet medication_, _eGFR (MDRD)_, _BMI_, _CAD history_, _stroke history_, _peripheral interventions_, _stenosis_, and _LDL_.

#### Natural log-transformed data

First we use the natural-log transformed data.

Analysis of continuous/quantitative plaque traits as a function of serum/plaque IL6(R)/MCP1 levels.
```{r CrossSec: plaques - linear regression MODEL3, include=TRUE, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")
for (protein in 1:length(TRAITS.PROTEIN)) {
  PROTEIN = TRAITS.PROTEIN[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(TRAITS.CON)) {
    TRAIT = TRAITS.CON[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M3) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    ### univariate
    fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender + 
                Hypertension.composite + DiabetesStatus + SmokerCurrent +
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
                CAD_history + Stroke_history + Peripheral.interv + stenose + LDL_final, 
              data = currentDF)
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
    GLM.results.TEMP[1,] = GLM.CON(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "T-value", "P-value", "r^2", "r^2_adj", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")
### Univariate
library(openxlsx)
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Con.Multi.Protein.PlaquePhenotypes.MODEL3.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Con.Multi.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)


```

Analysis of binary plaque traits as a function of serum/plaque IL6(R)/MCP1 levels.
```{r CrossSec: plaques - logistic regression MODEL3, include=TRUE, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN)) {
  PROTEIN = TRAITS.PROTEIN[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(TRAITS.BIN)) {
    TRAIT = TRAITS.BIN[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M3) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate
    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + 
                Hypertension.composite + DiabetesStatus + SmokerCurrent + 
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
                CAD_history + Stroke_history + Peripheral.interv + stenose + LDL_final, 
              data  =  currentDF, family = binomial(link = "logit"))
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")

### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Multi.Protein.PlaquePhenotypes.MODEL3.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Bin.Multi.PlaquePheno")

# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

```

#### Inverse-rank normalization

Here we use the inverse-rank normalized data - visually this is more normally distributed.

Analysis of continuous/quantitative plaque traits as a function of serum/plaque IL6(R)/MCP1 levels.
```{r CrossSec: plaques - linear regression MODEL3 RANK, include=TRUE, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(TRAITS.CON.RANK)) {
    TRAIT = TRAITS.CON.RANK[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M3) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    ### univariate
    fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender + 
                Hypertension.composite + DiabetesStatus + SmokerCurrent + 
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
                CAD_history + Stroke_history + Peripheral.interv + stenose + LDL_final, 
              data = currentDF)
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
    GLM.results.TEMP[1,] = GLM.CON(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "T-value", "P-value", "r^2", "r^2_adj", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")
### Univariate
library(openxlsx)
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Con.Multi.Protein.PlaquePhenotypes.RANK.MODEL3.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Con.Multi.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)


```

Analysis of binary plaque traits as a function of serum/plaque IL6(R)/MCP1 levels.
```{r CrossSec: plaques - logistic regression MODEL3 RANK, include=TRUE, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(TRAITS.BIN)) {
    TRAIT = TRAITS.BIN[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M3) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate
    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + 
                Hypertension.composite + DiabetesStatus + SmokerCurrent + 
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
                CAD_history + Stroke_history + Peripheral.interv + stenose + LDL_final, 
              data  =  currentDF, family = binomial(link = "logit"))
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")

### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Multi.Protein.PlaquePhenotypes.RANK.MODEL3.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Bin.Multi.PlaquePheno")

# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

```

### Model 4

In this model we correct for _Age_, _Gender_, _Hypertension status_, _Diabetes status_, _current smoker status_, _lipid-lowering drugs (LLDs)_, _antiplatelet medication_, _eGFR (MDRD)_, _BMI_, _CAD history_, _stroke history_, _peripheral interventions_, _stenosis_, and _hsCRP_.

#### Natural log-transformed data

First we use the natural-log transformed data.

Analysis of continuous/quantitative plaque traits as a function of serum/plaque IL6(R)/MCP1 levels.
```{r CrossSec: plaques - linear regression MODEL4, include=TRUE, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")
for (protein in 1:length(TRAITS.PROTEIN)) {
  PROTEIN = TRAITS.PROTEIN[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(TRAITS.CON)) {
    TRAIT = TRAITS.CON[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M4) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    ### univariate
    fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender + 
                Hypertension.composite + DiabetesStatus + SmokerCurrent +
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
                CAD_history + Stroke_history + Peripheral.interv + stenose + hsCRP_plasma, 
              data = currentDF)
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
    GLM.results.TEMP[1,] = GLM.CON(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "T-value", "P-value", "r^2", "r^2_adj", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")
### Univariate
library(openxlsx)
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Con.Multi.Protein.PlaquePhenotypes.MODEL4.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Con.Multi.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)


```

Analysis of binary plaque traits as a function of serum/plaque IL6(R)/MCP1 levels.
```{r CrossSec: plaques - logistic regression MODEL4, include=TRUE, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN)) {
  PROTEIN = TRAITS.PROTEIN[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(TRAITS.BIN)) {
    TRAIT = TRAITS.BIN[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M4) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate
    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + 
                Hypertension.composite + DiabetesStatus + SmokerCurrent + 
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
                CAD_history + Stroke_history + Peripheral.interv + stenose + hsCRP_plasma, 
              data  =  currentDF, family = binomial(link = "logit"))
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")

### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Multi.Protein.PlaquePhenotypes.MODEL4.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Bin.Multi.PlaquePheno")

# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

```

#### Inverse-rank normalization

Here we use the inverse-rank normalized data - visually this is more normally distributed.

Analysis of continuous/quantitative plaque traits as a function of serum/plaque IL6(R)/MCP1 levels.
```{r CrossSec: plaques - linear regression MODEL4 RANK, include=TRUE, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(TRAITS.CON.RANK)) {
    TRAIT = TRAITS.CON.RANK[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M4) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    ### univariate
    fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender + 
                Hypertension.composite + DiabetesStatus + SmokerCurrent + 
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
                CAD_history + Stroke_history + Peripheral.interv + stenose + hsCRP_plasma, 
              data = currentDF)
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
    GLM.results.TEMP[1,] = GLM.CON(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "T-value", "P-value", "r^2", "r^2_adj", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")
### Univariate
library(openxlsx)
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Con.Multi.Protein.PlaquePhenotypes.RANK.MODEL4.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Con.Multi.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)


```

Analysis of binary plaque traits as a function of serum/plaque IL6(R)/MCP1 levels.
```{r CrossSec: plaques - logistic regression MODEL4 RANK, include=TRUE, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(TRAITS.BIN)) {
    TRAIT = TRAITS.BIN[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M4) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate
    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + 
                Hypertension.composite + DiabetesStatus + SmokerCurrent + 
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
                CAD_history + Stroke_history + Peripheral.interv + stenose + hsCRP_plasma, 
              data  =  currentDF, family = binomial(link = "logit"))
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")

### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Multi.Protein.PlaquePhenotypes.RANK.MODEL4.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Bin.Multi.PlaquePheno")

# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

```

### Model 5

In this model we correct for _Age_, _Gender_, _Hypertension status_, _Diabetes status_, _current smoker status_, _lipid-lowering drugs (LLDs)_, _antiplatelet medication_, _eGFR (MDRD)_, _BMI_, _CAD history_, _stroke history_, _peripheral interventions_, _stenosis_, and _IL6 in plaques_.

#### Natural log-transformed data

First we use the natural-log transformed data.

Analysis of continuous/quantitative plaque traits as a function of serum/plaque IL6(R)/MCP1 levels.
```{r CrossSec: plaques - linear regression MODEL5, include=TRUE, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")
for (protein in 1:length(TRAITS.PROTEIN)) {
  PROTEIN = TRAITS.PROTEIN[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(TRAITS.CON)) {
    TRAIT = TRAITS.CON[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M5) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    ### univariate
    fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender + 
                Hypertension.composite + DiabetesStatus + SmokerCurrent +
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
                CAD_history + Stroke_history + Peripheral.interv + stenose + IL6_pg_ug_2015_LN, 
              data = currentDF)
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
    GLM.results.TEMP[1,] = GLM.CON(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "T-value", "P-value", "r^2", "r^2_adj", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")
### Univariate
library(openxlsx)
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Con.Multi.Protein.PlaquePhenotypes.MODEL5.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Con.Multi.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)


```

Analysis of binary plaque traits as a function of serum/plaque IL6(R)/MCP1 levels.
```{r CrossSec: plaques - logistic regression MODEL5, include=TRUE, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN)) {
  PROTEIN = TRAITS.PROTEIN[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(TRAITS.BIN)) {
    TRAIT = TRAITS.BIN[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M5) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate
    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + 
                Hypertension.composite + DiabetesStatus + SmokerCurrent + 
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
                CAD_history + Stroke_history + Peripheral.interv + stenose + IL6_pg_ug_2015_LN, 
              data  =  currentDF, family = binomial(link = "logit"))
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")

### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Multi.Protein.PlaquePhenotypes.MODEL5.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Bin.Multi.PlaquePheno")

# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

```

#### Inverse-rank normalization

Here we use the inverse-rank normalized data - visually this is more normally distributed.

Analysis of continuous/quantitative plaque traits as a function of serum/plaque IL6(R)/MCP1 levels.
```{r CrossSec: plaques - linear regression MODEL5 RANK, include=TRUE, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(TRAITS.CON.RANK)) {
    TRAIT = TRAITS.CON.RANK[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M5rank) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    ### univariate
    fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender + 
                Hypertension.composite + DiabetesStatus + SmokerCurrent + 
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
                CAD_history + Stroke_history + Peripheral.interv + stenose + IL6_pg_ug_2015_rank, 
              data = currentDF)
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
    GLM.results.TEMP[1,] = GLM.CON(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "T-value", "P-value", "r^2", "r^2_adj", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")
### Univariate
library(openxlsx)
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Con.Multi.Protein.PlaquePhenotypes.RANK.MODEL5.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Con.Multi.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)


```

Analysis of binary plaque traits as a function of serum/plaque IL6(R)/MCP1 levels.
```{r CrossSec: plaques - logistic regression MODEL5 RANK, include=TRUE, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(TRAITS.BIN)) {
    TRAIT = TRAITS.BIN[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M5rank) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate
    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + 
                Hypertension.composite + DiabetesStatus + SmokerCurrent + 
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
                CAD_history + Stroke_history + Peripheral.interv + stenose + IL6_pg_ug_2015_rank, 
              data  =  currentDF, family = binomial(link = "logit"))
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")

### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Multi.Protein.PlaquePhenotypes.RANK.MODEL5.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Bin.Multi.PlaquePheno")

# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

```


## B. Cross-sectional analysis symptoms

We will perform a cross-sectional analysis between plaque and serum MCP1, IL6, and IL6R levels and the 'clinical status' of the plaque in terms of presence of patients' symptoms (symptomatic vs. asymptomatic). The symptoms of interest are:

- stroke
- TIA
- retinal infarction
- amaurosis fugax
- asymptomatic

### Model 1

In this model we correct for _Age_, and _Gender_.

#### Natural log-transformed data

First we use the natural-log transformed data.

```{r CrossSec: symptoms - logistic regression MODEL1, include=TRUE, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN)) {
  PROTEIN = TRAITS.PROTEIN[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  TRAIT = "AsymptSympt"
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M1) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate
     # + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
     #            Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
     #            CAD_history + Stroke_history + Peripheral.interv + stenose
    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender, 
              data  =  currentDF, family = binomial(link = "logit"))
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")

### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Uni.Protein.Symptoms.MODEL1.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Bin.Uni.Symptoms")

# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

```

#### Inverse-rank normalization

Here we use the inverse-rank normalized data - visually this is more normally distributed.

```{r CrossSec: symptoms - logistic regression MODEL1 RANK, include=TRUE, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  TRAIT = "AsymptSympt"
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M1) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate
     # + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
     #            Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
     #            CAD_history + Stroke_history + Peripheral.interv + stenose
    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender, 
              data  =  currentDF, family = binomial(link = "logit"))
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")

### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Uni.Protein.RANK.Symptoms.MODEL1.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Bin.Uni.Symptoms")

# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

```

### Model 2

In this model we correct for _Age_, _Gender_, _Hypertension status_, _Diabetes status_, _current smoker status_, _lipid-lowering drugs (LLDs)_, _antiplatelet medication_, _eGFR (MDRD)_, _BMI_, _CAD history_, _stroke history_, _peripheral interventions_, and _stenosis._.

#### Natural log-transformed data

First we use the natural-log transformed data.

```{r CrossSec: symptoms - logistic regression MODEL2, include=TRUE, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN)) {
  PROTEIN = TRAITS.PROTEIN[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  TRAIT = "AsymptSympt"
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M2) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate
     
    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
                CAD_history + Stroke_history + Peripheral.interv + stenose, 
              data  =  currentDF, family = binomial(link = "logit"))
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")

### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Multi.Protein.Symptoms.MODEL2.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Bin.Multi.Symptoms")

# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

```

#### Inverse-rank normalization

Here we use the inverse-rank normalized data - visually this is more normally distributed.

```{r CrossSec: symptoms - logistic regression MODEL2 RANK, include=TRUE, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  TRAIT = "AsymptSympt"
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M2) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate

    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
                CAD_history + Stroke_history + Peripheral.interv + stenose, 
              data  =  currentDF, family = binomial(link = "logit"))
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")

### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Multi.Protein.RANK.Symptoms.MODEL2.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Bin.Multi.Symptoms")

# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

```

### Model 3

In this model we correct for _Age_, _Gender_, _Hypertension status_, _Diabetes status_, _current smoker status_, _lipid-lowering drugs (LLDs)_, _antiplatelet medication_, _eGFR (MDRD)_, _BMI_, _CAD history_, _stroke history_, _peripheral interventions_, _stenosis._, and _LDL_.

#### Natural log-transformed data

First we use the natural-log transformed data.

```{r CrossSec: symptoms - logistic regression MODEL3, include=TRUE, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN)) {
  PROTEIN = TRAITS.PROTEIN[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  TRAIT = "AsymptSympt"
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M3) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate
     
    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
                CAD_history + Stroke_history + Peripheral.interv + stenose + LDL_final, 
              data  =  currentDF, family = binomial(link = "logit"))
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")

### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Multi.Protein.Symptoms.MODEL3.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Bin.Multi.Symptoms")

# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

```

#### Inverse-rank normalization

Here we use the inverse-rank normalized data - visually this is more normally distributed.

```{r CrossSec: symptoms - logistic regression MODEL3 RANK, include=TRUE, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  TRAIT = "AsymptSympt"
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M3) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate

    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
                CAD_history + Stroke_history + Peripheral.interv + stenose + LDL_final, 
              data  =  currentDF, family = binomial(link = "logit"))
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")

### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Multi.Protein.RANK.Symptoms.MODEL3.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Bin.Multi.Symptoms")

# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

```

### Model 4

In this model we correct for _Age_, _Gender_, _Hypertension status_, _Diabetes status_, _current smoker status_, _lipid-lowering drugs (LLDs)_, _antiplatelet medication_, _eGFR (MDRD)_, _BMI_, _CAD history_, _stroke history_, _peripheral interventions_, _stenosis._, and _hsCRP_.

#### Natural log-transformed data

First we use the natural-log transformed data.

```{r CrossSec: symptoms - logistic regression MODEL4, include=TRUE, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN)) {
  PROTEIN = TRAITS.PROTEIN[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  TRAIT = "AsymptSympt"
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M4) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate
     
    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
                CAD_history + Stroke_history + Peripheral.interv + stenose + hsCRP_plasma, 
              data  =  currentDF, family = binomial(link = "logit"))
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")

### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Multi.Protein.RANK.Symptoms.MODEL4.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Bin.Multi.Symptoms")

# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

```

#### Inverse-rank normalization

Here we use the inverse-rank normalized data - visually this is more normally distributed.

```{r CrossSec: symptoms - logistic regression MODEL4 RANK, include=TRUE, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  TRAIT = "AsymptSympt"
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M4) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate

    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
                CAD_history + Stroke_history + Peripheral.interv + stenose + hsCRP_plasma, 
              data  =  currentDF, family = binomial(link = "logit"))
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")

### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Multi.Protein.RANK.Symptoms.MODEL4.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Bin.Multi.Symptoms")

# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

```

### Model 5

In this model we correct for _Age_, _Gender_, _Hypertension status_, _Diabetes status_, _current smoker status_, _lipid-lowering drugs (LLDs)_, _antiplatelet medication_, _eGFR (MDRD)_, _BMI_, _CAD history_, _stroke history_, _peripheral interventions_, _stenosis._, and _IL6 in plaques_.

#### Natural log-transformed data

First we use the natural-log transformed data.

```{r CrossSec: symptoms - logistic regression MODEL5, include=TRUE, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN)) {
  PROTEIN = TRAITS.PROTEIN[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  TRAIT = "AsymptSympt"
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M5) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate
     
    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
                CAD_history + Stroke_history + Peripheral.interv + stenose + IL6_pg_ug_2015_LN, 
              data  =  currentDF, family = binomial(link = "logit"))
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")

### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Multi.Protein.RANK.Symptoms.MODEL5.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Bin.Multi.Symptoms")

# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

```

#### Inverse-rank normalization

Here we use the inverse-rank normalized data - visually this is more normally distributed.

```{r CrossSec: symptoms - logistic regression MODEL5 RANK, include=TRUE, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  TRAIT = "AsymptSympt"
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M5rank) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate

    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
                CAD_history + Stroke_history + Peripheral.interv + stenose + IL6_pg_ug_2015_rank, 
              data  =  currentDF, family = binomial(link = "logit"))
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")

### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Multi.Protein.RANK.Symptoms.MODEL5.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Bin.Multi.Symptoms")

# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

```


## C. Longitudinal analysis secondary clinical outcome

For the longitudinal analyses of plaque and serum MCP1, IL6, and IL6R levels and secondary cardiovascular events over a three-year follow-up period. 

The _primary outcome_ is defined as "a composite of fatal or non-fatal myocardial infarction, fatal or non-fatal stroke, ruptured aortic aneurysm, fatal cardiac failure, coronary or peripheral interventions, leg amputation due to vascular causes, and cardiovascular death", i.e. major adverse cardiovascular events (MACE). Variable: `epmajor.3years`, these include:
- myocardial infarction (MI)
- cerebral infarction (CVA/stroke)
- cardiovascular death (exact cause to be investigated)
- cerebral bleeding (CVA/stroke)
- fatal myocardial infarction (MI)
- fatal cerebral infarction
- fatal cerebral bleeding
- sudden death
- fatal heart failure
- fatal aneurysm rupture
- other cardiovascular death..

The _secondary outcomes_ will be 

- incidence of fatal or non-fatal stroke (ischemic and bleeding) - variable: `epstroke.3years`, these include:
  - cerebral infarction (CVA/stroke)
  - cerebral bleeding (CVA/stroke)
  - fatal cerebral infarction
  - fatal cerebral bleeding.
- incidence of acute coronary events (fatal or non-fatal myocardial infarction, coronary interventions) - variable: `epcoronary.3years`, these include:
  - myocardial infarction (MI)
  - coronary angioplasty (PCI/PTCA)
  - cardiovascular death (exact cause to be investigated)
  - coronary bypass (CABG)
  - fatal myocardial infarction (MI)
  - sudden death.
- cardiovascular death - variable: `epcvdeath.3years`, these include:
  - cardiovascular death (exact cause to be investigated)
  - fatal myocardial infarction (MI)
  - fatal cerebral infarction
  - fatal cerebral bleeding
  - sudden death
  - fatal heart failure
  - fatal aneurysm rupture
  - other cardiovascular death..

### Sanity checks

First we do some sanity checks and inventory the time-to-event and event variables.

```{r Cox-regressions: General}
# Reference: https://bioconductor.org/packages/devel/bioc/vignettes/MultiAssayExperiment/inst/doc/QuickStartMultiAssay.html
# If you want to suppress warnings and messages when installing/loading packages
# suppressPackageStartupMessages({})
install.packages.auto("survival")
install.packages.auto("survminer")
install.packages.auto("Hmisc")

cat("* Creating function to summarize Cox regression and prepare container for results.")
# Function to get summary statistics from Cox regression model
COX.STAT <- function(coxfit, DATASET, OUTCOME, protein){
  cat("Summarizing Cox regression results for '", protein ,"' and its association to '",OUTCOME,"' in '",DATASET,"'.\n")
  if (nrow(summary(coxfit)$coefficients) == 1) {
    output = c(protein, rep(NA,8))
    cat("Model not fitted; probably singular.\n")
  }else {
    cat("Collecting data.\n\n")
    cox.sum <- summary(coxfit)
    cox.effectsize = cox.sum$coefficients[1,1]
    cox.SE = cox.sum$coefficients[1,3]
    cox.HReffect = cox.sum$coefficients[1,2]
    cox.CI_low = exp(cox.effectsize - 1.96 * cox.SE)
    cox.CI_up = exp(cox.effectsize + 1.96 * cox.SE)
    cox.zvalue = cox.sum$coefficients[1,4]
    cox.pvalue = cox.sum$coefficients[1,5]
    cox.sample_size = cox.sum$n
    cox.nevents = cox.sum$nevent
    
    output = c(DATASET, OUTCOME, protein, cox.effectsize, cox.SE, cox.HReffect, cox.CI_low, cox.CI_up, cox.zvalue, cox.pvalue, cox.sample_size, cox.nevents)
    cat("We have collected the following:\n")
    cat("Dataset used..............:", DATASET, "\n")
    cat("Outcome analyzed..........:", OUTCOME, "\n")
    cat("Protein...................:", protein, "\n")
    cat("Effect size...............:", round(cox.effectsize, 6), "\n")
    cat("Standard error............:", round(cox.SE, 6), "\n")
    cat("Odds ratio (effect size)..:", round(cox.HReffect, 3), "\n")
    cat("Lower 95% CI..............:", round(cox.CI_low, 3), "\n")
    cat("Upper 95% CI..............:", round(cox.CI_up, 3), "\n")
    cat("T-value...................:", round(cox.zvalue, 6), "\n")
    cat("P-value...................:", signif(cox.pvalue, 8), "\n")
    cat("Sample size in model......:", cox.sample_size, "\n")
    cat("Number of events..........:", cox.nevents, "\n")
  }
  return(output)
  print(output)
} 

times = c("ep_major_t_3years", 
          "ep_stroke_t_3years", "ep_coronary_t_3years", "ep_cvdeath_t_3years")

endpoints = c("epmajor.3years", 
              "epstroke.3years", "epcoronary.3years", "epcvdeath.3years")

cat("* Check the cases per event type - for sanity.")
for (events in endpoints){
  print(paste0("Printing the summary of: ",events))
  print(summary(AEDB.CEA[,events]))
  print(table(AEDB.CEA[,events]))
}

cat("* Check distribution of events over time - for sanity.")
for (eventtimes in times){
  print(paste0("Printing the summary of: ",eventtimes))
  print(summary(AEDB.CEA[,eventtimes]))
}

for (eventtime in times){
  
  print(paste0("Printing the distribution of: ",eventtime))
  p <- gghistogram(AEDB.CEA, x = eventtime, y = "..count..",
              main = eventtime, bins = 15, 
              xlab = "year", color = uithof_color[16], fill = uithof_color[16], ggtheme = theme_minimal()) 
 print(p)
 ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.EventDistributionPerYear.",eventtime,".pdf"), plot = last_plot())
}


```


### Cox regressions

Let's perform the actual Cox-regressions. We will apply a couple of models: 

- Model 1: adjusted for age and sex
- Model 2: adjusted for age, sex, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis,
- Model 3: same to model 2, with additional adjustments for circulating CRP levels
- Model 4: same to model 2 with additional adjustment for IL6 levels in the plaque


*MODEL 1*
```{r Cox-regression Analysis: Simple model}
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))

# Looping over each protein/endpoint/time combination
for (i in 1:length(times)){
  eptime = times[i]
  ep = endpoints[i]
  cat(paste0("* Analyzing the effect of plaque proteins on [",ep,"].\n"))
  cat(" - creating temporary SE for this work.\n")
  TEMP.DF = as.data.frame(AEDB.CEA)
  cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
  TEMP.DF$event <- as.integer(TEMP.DF[,ep])
  TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
  cat(" - making strata of each of the plaque proteins and start survival analysis.\n")
  
  for (protein in 1:length(TRAITS.PROTEIN.RANK)){
    cat(paste0("   > processing [",TRAITS.PROTEIN.RANK[protein],"]; ",protein," out of ",length(TRAITS.PROTEIN.RANK)," proteins.\n"))
    # splitting into two groups
    TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]] <- cut2(TEMP.DF[,TRAITS.PROTEIN.RANK[protein]], g = 2)
    cat(paste0("   > cross tabulation of ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    show(table(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]))
    
    cat(paste0("\n   > fitting the model for ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    fit <- survfit(as.formula(paste0("y ~ ", TRAITS.PROTEIN.RANK[protein])), data = TEMP.DF)
    
    cat(paste0("\n   > make a Kaplan-Meier-shizzle...\n"))
    # make Kaplan-Meier curve and save it
    show(ggsurvplot(fit, data = TEMP.DF,
                    palette = c("#DB003F", "#1290D9"),
                    # palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
                    linetype = c(1,2),
                    # linetype = c(1,2,3,4),
                    # conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
                    pval = FALSE, pval.method = FALSE, pval.size = 4,
                    risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
                    censor = FALSE,
                    legend = "right",
                    legend.title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
                    legend.labs = c("low", "high"),
                    title = paste0("Risk of ",ep,""), xlab = "Time [years]", font.main = c(16, "bold", "black")))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.survival.",ep,".2G.",
                               TRAITS.PROTEIN.RANK[protein],".pdf"), width = 12, height = 10, onefile = FALSE)

    cat(paste0("\n   > perform the Cox-regression fashizzle and plot it...\n"))
    ### Do Cox-regression and plot it
    
    ### MODEL 1 (Simple model)
    cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]+Age+Gender, data = TEMP.DF)
    coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]])+Age+Gender, data = TEMP.DF)

    plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
         # ylim = c(0.2, 1), xlim = c(0,3), col = c("#595A5C", "#DB003F", "#1290D9"),
         ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         lty = c(1,2), lwd = 2,
         ylab = "Suvival probability", xlab = "FU time [years]",
         mark.time = FALSE, axes = FALSE, bty = "n")
    legend("topright",
           c("low", "high"),
           title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
           col = c("#DB003F", "#1290D9"),
           lty = c(1,2), lwd = 2,
           bty = "n")
    axis(side = 1, at = seq(0, 3, by = 1))
    axis(side = 2, at = seq(0, 1, by = 0.2))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.Cox.",ep,".2G.",
                               # Today,".AEDB.CEA.Cox.",ep,".4G.",
                               TRAITS.PROTEIN.RANK[protein],".MODEL1.pdf"), height = 12, width = 10, onefile = TRUE)
    show(summary(cox))

    cat(paste0("\n   > writing the Cox-regression fashizzle to Excel...\n"))

    COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
    COX.results.TEMP[1,] = COX.STAT(cox, "AEDB.CEA", ep, TRAITS.PROTEIN.RANK[protein])
    COX.results = rbind(COX.results, COX.results.TEMP)

  }
}

cat("- Edit the column names...\n")
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
                          "Beta", "s.e.m.",
                          "HR", "low95CI", "up95CI",
                          "Z-value", "P-value", "SampleSize", "N_events")

cat("- Correct the variable types...\n")
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)

AEDB.CEA.COX.results <- COX.results

# Save the data
cat("- Writing results to Excel-file...\n")
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AEDB.CEA.COX.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Cox.2G.MODEL1.xlsx"),
           creator = "Sander W. van der Laan",
           sheetName = "Results", headerStyle = head.style,
           row.names = FALSE, col.names = TRUE, overwrite = TRUE)

# Removing intermediates
cat("- Removing intermediate files...\n")
#rm(TEMP.DF, protein, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AEDB.CEA.COX.results)

#rm(head.style)

```

*MODEL 2*
```{r Cox-regression Analysis: MODEL 2}
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))

# Looping over each protein/endpoint/time combination
for (i in 1:length(times)){
  eptime = times[i]
  ep = endpoints[i]
  cat(paste0("* Analyzing the effect of plaque proteins on [",ep,"].\n"))
  cat(" - creating temporary SE for this work.\n")
  TEMP.DF = as.data.frame(AEDB.CEA)
  cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
  TEMP.DF$event <- as.integer(TEMP.DF[,ep])
  #as.integer(TEMP.DF[,ep] == "Excluded")

  TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
  cat(" - making strata of each of the plaque proteins and start survival analysis.\n")
  
  for (protein in 1:length(TRAITS.PROTEIN.RANK)){
    cat(paste0("   > processing [",TRAITS.PROTEIN.RANK[protein],"]; ",protein," out of ",length(TRAITS.PROTEIN.RANK)," proteins.\n"))
    # splitting into two groups
    TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]] <- cut2(TEMP.DF[,TRAITS.PROTEIN.RANK[protein]], g = 2)
    cat(paste0("   > cross tabulation of ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    show(table(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]))
    
    cat(paste0("\n   > fitting the model for ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    fit <- survfit(as.formula(paste0("y ~ ", TRAITS.PROTEIN.RANK[protein])), data = TEMP.DF)
    
    cat(paste0("\n   > make a Kaplan-Meier-shizzle...\n"))
    # make Kaplan-Meier curve and save it
    show(ggsurvplot(fit, data = TEMP.DF,
                    palette = c("#DB003F", "#1290D9"),
                    # palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
                    linetype = c(1,2),
                    # linetype = c(1,2,3,4),
                    # conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
                    pval = FALSE, pval.method = FALSE, pval.size = 4,
                    risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
                    censor = FALSE,
                    legend = "right",
                    legend.title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
                    legend.labs = c("low", "high"),
                    title = paste0("Risk of ",ep,""), xlab = "Time [years]", font.main = c(16, "bold", "black")))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.survival.",ep,".2G.",
                               TRAITS.PROTEIN.RANK[protein],".pdf"), width = 12, height = 10, onefile = FALSE)

    cat(paste0("\n   > perform the Cox-regression fashizzle and plot it...\n"))
    ### Do Cox-regression and plot it
    
    ### MODEL 2 adjusted for age, sex, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis
    cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]+Age + Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + CAD_history + Stroke_history + Peripheral.interv + stenose, data = TEMP.DF)
    coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]])+Age + Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + CAD_history + Stroke_history + Peripheral.interv + stenose, data = TEMP.DF)

  
    plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
         # ylim = c(0.2, 1), xlim = c(0,3), col = c("#595A5C", "#DB003F", "#1290D9"),
         ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         lty = c(1,2), lwd = 2,
         ylab = "Suvival probability", xlab = "FU time [years]",
         mark.time = FALSE, axes = FALSE, bty = "n")
    legend("topright",
           c("low", "high"),
           title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
           col = c("#DB003F", "#1290D9"),
           lty = c(1,2), lwd = 2,
           bty = "n")
    axis(side = 1, at = seq(0, 3, by = 1))
    axis(side = 2, at = seq(0, 1, by = 0.2))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.Cox.",ep,".2G.",
                               # Today,".AEDB.CEA.Cox.",ep,".4G.",
                               TRAITS.PROTEIN.RANK[protein],".MODEL2.pdf"), height = 12, width = 10, onefile = TRUE)

    show(summary(cox))

    cat(paste0("\n   > writing the Cox-regression fashizzle to Excel...\n"))

    COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
    COX.results.TEMP[1,] = COX.STAT(cox, "AEDB.CEA", ep, TRAITS.PROTEIN.RANK[protein])
    COX.results = rbind(COX.results, COX.results.TEMP)

  }
}

cat("- Edit the column names...\n")
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
                          "Beta", "s.e.m.",
                          "HR", "low95CI", "up95CI",
                          "Z-value", "P-value", "SampleSize", "N_events")

cat("- Correct the variable types...\n")
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)

AEDB.CEA.COX.results <- COX.results

# Save the data
cat("- Writing results to Excel-file...\n")
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AEDB.CEA.COX.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Cox.2G.MODEL2.xlsx"),
           creator = "Sander W. van der Laan",
           sheetName = "Results", headerStyle = head.style,
           row.names = FALSE, col.names = TRUE, overwrite = TRUE)

# Removing intermediates
cat("- Removing intermediate files...\n")
rm(TEMP.DF, protein, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AEDB.CEA.COX.results)

rm(head.style)

```

*MODEL 3*
```{r Cox-regression Analysis: MODEL 3}
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))

# Looping over each protein/endpoint/time combination
for (i in 1:length(times)){
  eptime = times[i]
  ep = endpoints[i]
  cat(paste0("* Analyzing the effect of plaque proteins on [",ep,"].\n"))
  cat(" - creating temporary SE for this work.\n")
  TEMP.DF = as.data.frame(AEDB.CEA)
  cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
  TEMP.DF$event <- as.integer(TEMP.DF[,ep])
  #as.integer(TEMP.DF[,ep] == "Excluded")

  TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
  cat(" - making strata of each of the plaque proteins and start survival analysis.\n")
  
  for (protein in 1:length(TRAITS.PROTEIN.RANK)){
    cat(paste0("   > processing [",TRAITS.PROTEIN.RANK[protein],"]; ",protein," out of ",length(TRAITS.PROTEIN.RANK)," proteins.\n"))
    # splitting into two groups
    TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]] <- cut2(TEMP.DF[,TRAITS.PROTEIN.RANK[protein]], g = 2)
    cat(paste0("   > cross tabulation of ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    show(table(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]))
    
    cat(paste0("\n   > fitting the model for ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    fit <- survfit(as.formula(paste0("y ~ ", TRAITS.PROTEIN.RANK[protein])), data = TEMP.DF)
    
    cat(paste0("\n   > make a Kaplan-Meier-shizzle...\n"))
    # make Kaplan-Meier curve and save it
    show(ggsurvplot(fit, data = TEMP.DF,
                    palette = c("#DB003F", "#1290D9"),
                    # palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
                    linetype = c(1,2),
                    # linetype = c(1,2,3,4),
                    # conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
                    pval = FALSE, pval.method = FALSE, pval.size = 4,
                    risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
                    censor = FALSE,
                    legend = "right",
                    legend.title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
                    legend.labs = c("low", "high"),
                    title = paste0("Risk of ",ep,""), xlab = "Time [years]", font.main = c(16, "bold", "black")))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.survival.",ep,".2G.",
                               TRAITS.PROTEIN.RANK[protein],".pdf"), width = 12, height = 10, onefile = FALSE)

    cat(paste0("\n   > perform the Cox-regression fashizzle and plot it...\n"))
    ### Do Cox-regression and plot it
    
    ### MODEL 3 same to model 2, with additional adjustments for circulating CRP levels
    cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]+Age + Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + CAD_history + Stroke_history + Peripheral.interv + stenose + hsCRP_plasma, data = TEMP.DF)
    coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]])+Age + Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +  Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + CAD_history + Stroke_history + Peripheral.interv + stenose + hsCRP_plasma, data = TEMP.DF)

  
    plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
         # ylim = c(0.2, 1), xlim = c(0,3), col = c("#595A5C", "#DB003F", "#1290D9"),
         ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         lty = c(1,2), lwd = 2,
         ylab = "Suvival probability", xlab = "FU time [years]",
         mark.time = FALSE, axes = FALSE, bty = "n")
    legend("topright",
           c("low", "high"),
           title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
           col = c("#DB003F", "#1290D9"),
           lty = c(1,2), lwd = 2,
           bty = "n")
    axis(side = 1, at = seq(0, 3, by = 1))
    axis(side = 2, at = seq(0, 1, by = 0.2))

    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.Cox.",ep,".2G.",
                               # Today,".AEDB.CEA.Cox.",ep,".4G.",
                               TRAITS.PROTEIN.RANK[protein],".MODEL3.pdf"), height = 12, width = 10, onefile = TRUE)

    show(summary(cox))

    cat(paste0("\n   > writing the Cox-regression fashizzle to Excel...\n"))

    COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
    COX.results.TEMP[1,] = COX.STAT(cox, "AEDB.CEA", ep, TRAITS.PROTEIN.RANK[protein])
    COX.results = rbind(COX.results, COX.results.TEMP)

  }
}

cat("- Edit the column names...\n")
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
                          "Beta", "s.e.m.",
                          "HR", "low95CI", "up95CI",
                          "Z-value", "P-value", "SampleSize", "N_events")

cat("- Correct the variable types...\n")
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)

AEDB.CEA.COX.results <- COX.results

# Save the data
cat("- Writing results to Excel-file...\n")
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AEDB.CEA.COX.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Cox.2G.MODEL3.xlsx"),
           creator = "Sander W. van der Laan",
           sheetName = "Results", headerStyle = head.style,
           row.names = FALSE, col.names = TRUE, overwrite = TRUE)

# Removing intermediates
cat("- Removing intermediate files...\n")
rm(TEMP.DF, protein, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AEDB.CEA.COX.results)

rm(head.style)

```

*MODEL 4*
```{r Cox-regression Analysis: MODEL 4}
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))

# Looping over each protein/endpoint/time combination
for (i in 1:length(times)){
  eptime = times[i]
  ep = endpoints[i]
  cat(paste0("* Analyzing the effect of plaque proteins on [",ep,"].\n"))
  cat(" - creating temporary SE for this work.\n")
  TEMP.DF = as.data.frame(AEDB.CEA)
  cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
  TEMP.DF$event <- as.integer(TEMP.DF[,ep])
  #as.integer(TEMP.DF[,ep] == "Excluded")

  TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
  cat(" - making strata of each of the plaque proteins and start survival analysis.\n")
  
  for (protein in 1:length(TRAITS.PROTEIN.RANK)){
    cat(paste0("   > processing [",TRAITS.PROTEIN.RANK[protein],"]; ",protein," out of ",length(TRAITS.PROTEIN.RANK)," proteins.\n"))
    # splitting into two groups
    TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]] <- cut2(TEMP.DF[,TRAITS.PROTEIN.RANK[protein]], g = 2)
    cat(paste0("   > cross tabulation of ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    show(table(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]))
    
    cat(paste0("\n   > fitting the model for ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    fit <- survfit(as.formula(paste0("y ~ ", TRAITS.PROTEIN.RANK[protein])), data = TEMP.DF)
    
    cat(paste0("\n   > make a Kaplan-Meier-shizzle...\n"))
    # make Kaplan-Meier curve and save it
    show(ggsurvplot(fit, data = TEMP.DF,
                    palette = c("#DB003F", "#1290D9"),
                    # palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
                    linetype = c(1,2),
                    # linetype = c(1,2,3,4),
                    # conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
                    pval = FALSE, pval.method = FALSE, pval.size = 4,
                    risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
                    censor = FALSE,
                    legend = "right",
                    legend.title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
                    legend.labs = c("low", "high"),
                    title = paste0("Risk of ",ep,""), xlab = "Time [years]", font.main = c(16, "bold", "black")))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.survival.",ep,".2G.",
                               TRAITS.PROTEIN.RANK[protein],".pdf"), width = 12, height = 10, onefile = FALSE)

    cat(paste0("\n   > perform the Cox-regression fashizzle and plot it...\n"))
    ### Do Cox-regression and plot it
    
    ### MODEL 4 same to model 2 with additional adjustment for IL6 levels in the plaque
    cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]+Age + Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + CAD_history + Stroke_history + Peripheral.interv + stenose + IL6_pg_ug_2015_rank, data = TEMP.DF)
    coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]])+Age + Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + CAD_history + Stroke_history + Peripheral.interv + stenose + IL6_pg_ug_2015_rank, data = TEMP.DF)
  
    plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
         # ylim = c(0.2, 1), xlim = c(0,3), col = c("#595A5C", "#DB003F", "#1290D9"),
         ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         lty = c(1,2), lwd = 2,
         ylab = "Suvival probability", xlab = "FU time [years]",
         mark.time = FALSE, axes = FALSE, bty = "n")
    legend("topright",
           c("low", "high"),
           title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
           col = c("#DB003F", "#1290D9"),
           lty = c(1,2), lwd = 2,
           bty = "n")
    axis(side = 1, at = seq(0, 3, by = 1))
    axis(side = 2, at = seq(0, 1, by = 0.2))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.Cox.",ep,".2G.",
                               # Today,".AEDB.CEA.Cox.",ep,".4G.",
                               TRAITS.PROTEIN.RANK[protein],".MODEL4.pdf"), height = 12, width = 10, onefile = TRUE)
    show(summary(cox))

    cat(paste0("\n   > writing the Cox-regression fashizzle to Excel...\n"))

    COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
    COX.results.TEMP[1,] = COX.STAT(cox, "AEDB.CEA", ep, TRAITS.PROTEIN.RANK[protein])
    COX.results = rbind(COX.results, COX.results.TEMP)

  }
}

cat("- Edit the column names...\n")
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
                          "Beta", "s.e.m.",
                          "HR", "low95CI", "up95CI",
                          "Z-value", "P-value", "SampleSize", "N_events")

cat("- Correct the variable types...\n")
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)

AEDB.CEA.COX.results <- COX.results

# Save the data
cat("- Writing results to Excel-file...\n")
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AEDB.CEA.COX.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Cox.2G.MODEL4.xlsx"),
           creator = "Sander W. van der Laan",
           sheetName = "Results", headerStyle = head.style,
           row.names = FALSE, col.names = TRUE, overwrite = TRUE)

# Removing intermediates
cat("- Removing intermediate files...\n")
rm(TEMP.DF, protein, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AEDB.CEA.COX.results)

rm(head.style)

```




# Correlations

## All biomarkers
We correlated serum and plaque levels of the biomarkers.

```{r CrossSampleType Correlations}

# Installation of ggcorrplot()
# --------------------------------
if(!require(devtools)) 
  install.packages("devtools")
devtools::install_github("kassambara/ggcorrplot")

library(ggcorrplot)

# Creating matrix - natural log transformed
# --------------------------------
AEDB.CEA.temp <- subset(AEDB.CEA, 
                          select = c("IL6_LN", "MCP1_LN", "IL6_pg_ug_2015_LN", "MCP1_pg_ug_2015_LN", "IL6R_pg_ug_2015_LN",
                                     TRAITS.BIN, TRAITS.CON)
                                    )

AEDB.CEA.temp$CalcificationPlaque <- as.numeric(AEDB.CEA.temp$CalcificationPlaque)
AEDB.CEA.temp$CollagenPlaque <- as.numeric(AEDB.CEA.temp$CollagenPlaque)
AEDB.CEA.temp$Fat10Perc <- as.numeric(AEDB.CEA.temp$Fat10Perc)
AEDB.CEA.temp$IPH <- as.numeric(AEDB.CEA.temp$IPH)
str(AEDB.CEA.temp)
AEDB.CEA.matrix <- as.matrix(AEDB.CEA.temp)
rm(AEDB.CEA.temp)

corr_biomarkers <- round(cor(AEDB.CEA.matrix, 
                             use = "pairwise.complete.obs", #the correlation or covariance between each pair of variables is computed using all complete pairs of observations on those variables
                             method = "spearman"), 3)
# corr_biomarkers

corr_biomarkers_p <- ggcorrplot::cor_pmat(AEDB.CEA.matrix, use = "pairwise.complete.obs", method = "spearman")

# Add correlation coefficients
# --------------------------------
# argument lab = TRUE
ggcorrplot(corr_biomarkers, 
           method = "square", 
           type = "lower",
           title = "Cross biomarker correlations", 
           show.legend = TRUE, legend.title = bquote("Spearman's"~italic(rho)),
           ggtheme = ggplot2::theme_minimal, outline.color = "#FFFFFF",
           show.diag = TRUE,
           hc.order = FALSE, 
           lab = FALSE,
           digits = 3,
           # p.mat = corr_biomarkers_p, sig.level = 0.05,
           colors = c("#1290D9", "#FFFFFF", "#E55738"))

# Creating matrix - inverse-rank transformation
# --------------------------------
AEDB.CEA.temp <- subset(AEDB.CEA, 
                          select = c("IL6_rank", "MCP1_rank", "IL6_pg_ug_2015_rank", "MCP1_pg_ug_2015_rank", "IL6R_pg_ug_2015_rank",
                                               TRAITS.BIN, TRAITS.CON.RANK)
                                    )

AEDB.CEA.temp$CalcificationPlaque <- as.numeric(AEDB.CEA.temp$CalcificationPlaque)
AEDB.CEA.temp$CollagenPlaque <- as.numeric(AEDB.CEA.temp$CollagenPlaque)
AEDB.CEA.temp$Fat10Perc <- as.numeric(AEDB.CEA.temp$Fat10Perc)
AEDB.CEA.temp$IPH <- as.numeric(AEDB.CEA.temp$IPH)
str(AEDB.CEA.temp)
AEDB.CEA.matrix.RANK <- as.matrix(AEDB.CEA.temp)
rm(AEDB.CEA.temp)

corr_biomarkers.rank <- round(cor(AEDB.CEA.matrix.RANK, 
                             use = "pairwise.complete.obs", #the correlation or covariance between each pair of variables is computed using all complete pairs of observations on those variables
                             method = "spearman"), 3)
# corr_biomarkers.rank

corr_biomarkers_p.rank <- ggcorrplot::cor_pmat(AEDB.CEA.matrix.RANK, use = "pairwise.complete.obs", method = "spearman")

# Add correlation coefficients
# --------------------------------
# argument lab = TRUE
ggcorrplot(corr_biomarkers.rank, 
           method = "square", 
           type = "lower",
           title = "Cross biomarker correlations", 
           show.legend = TRUE, legend.title = bquote("Spearman's"~italic(rho)),
           ggtheme = ggplot2::theme_minimal, outline.color = "#FFFFFF",
           show.diag = TRUE,
           hc.order = FALSE, 
           lab = FALSE,
           digits = 3,
           # p.mat = corr_biomarkers_p.rank, sig.level = 0.05,
           colors = c("#1290D9", "#FFFFFF", "#E55738"))


# flattenCorrMatrix
# --------------------------------
# cormat : matrix of the correlation coefficients
# pmat : matrix of the correlation p-values
flattenCorrMatrix <- function(cormat, pmat) {
  ut <- upper.tri(cormat)
  data.frame(
    biomarker_row = rownames(cormat)[row(cormat)[ut]],
    biomarker_column = rownames(cormat)[col(cormat)[ut]],
    spearman_cor  =(cormat)[ut],
    pval = pmat[ut]
    )
}

corr_biomarkers.df <- as.data.table(flattenCorrMatrix(corr_biomarkers, corr_biomarkers_p))
DT::datatable(corr_biomarkers.df)

corr_biomarkers.rank.df <- as.data.table(flattenCorrMatrix(corr_biomarkers.rank, corr_biomarkers_p.rank))
DT::datatable(corr_biomarkers.rank.df)

# chart of a correlation matrix
# --------------------------------
# Alternative solution https://www.r-graph-gallery.com/199-correlation-matrix-with-ggally.html
install.packages.auto("PerformanceAnalytics")
chart.Correlation.new <- function (R, histogram = TRUE, method = c("pearson", "kendall", 
    "spearman"), ...) 
{
    x = checkData(R, method = "matrix")
    if (missing(method)) 
        method = method[1]
    cormeth <- method
    panel.cor <- function(x, y, digits = 2, prefix = "", use = "pairwise.complete.obs", 
        method = cormeth, cex.cor, ...) {
        usr <- par("usr")
        on.exit(par(usr))
        par(usr = c(0, 1, 0, 1))
        r <- cor(x, y, use = use, method = method)
        txt <- format(c(r, 0.123456789), digits = digits)[1]
        txt <- paste(prefix, txt, sep = "")
        if (missing(cex.cor)) 
            cex <- 0.8/strwidth(txt)
        test <- cor.test(as.numeric(x), as.numeric(y), method = method)
        Signif <- symnum(test$p.value, corr = FALSE, na = FALSE, 
            cutpoints = c(0, 0.001, 0.01, 0.05, 0.1, 1), symbols = c("***", 
                "**", "*", ".", " "))
        text(0.5, 0.5, txt, cex = cex * (abs(r) + 0.3)/1.3)
        text(0.8, 0.8, Signif, cex = cex, col = 2)
    }
    f <- function(t) {
        dnorm(t, mean = mean(x), sd = sd.xts(x))
    }
    dotargs <- list(...)
    dotargs$method <- NULL
    rm(method)
    hist.panel = function(x, ... = NULL) {
        par(new = TRUE)
        hist(x, col = "#1290D9", probability = TRUE, axes = FALSE, 
        # hist(x, col = "light gray", probability = TRUE, axes = FALSE, 
            main = "", breaks = "FD")
        lines(density(x, na.rm = TRUE), col = "#E55738", lwd = 1)
        rug(x)
    }
    if (histogram) 
        pairs(x, gap = 0, lower.panel = panel.smooth, upper.panel = panel.cor, 
            diag.panel = hist.panel, ...)
    else pairs(x, gap = 0, lower.panel = panel.smooth, upper.panel = panel.cor, ...)
}

chart.Correlation.new(AEDB.CEA.matrix, method = "spearman", histogram = TRUE, pch = 3)

chart.Correlation.new(AEDB.CEA.matrix.RANK, method = "spearman", histogram = TRUE, pch = 3)


# alternative chart of a correlation matrix
# --------------------------------
# Alternative solution https://www.r-graph-gallery.com/199-correlation-matrix-with-ggally.html
install.packages.auto("GGally")

# Quick display of two cabapilities of GGally, to assess the distribution and correlation of variables 
library(GGally)
 
# From the help page:
ggpairs(AEDB.CEA, 
        columns = c("IL6_rank", "MCP1_rank", "IL6_pg_ug_2015_rank", "MCP1_pg_ug_2015_rank", "IL6R_pg_ug_2015_rank", TRAITS.BIN, TRAITS.CON.RANK), 
        columnLabels = c("IL6 (serum)", "MCP1 (serum)", "IL6", "MCP1", "IL6R",
                         "Calcification", "Collagen", "Fat 10%", "IPH", "Macrophages", "SMC", "Vessel density"),
        method = c("spearman"),
        # ggplot2::aes(colour = Gender),
        progress = FALSE) 
```

## Circulating MCP1

Finally, we explored in a sub-sample, where circulating MCP-1 levels are available, the following:

1. A correlation between MCP-1 levels in the plaque and circulating MCP-1 levels
2. Associations of circulating MCP-1 levels with plaque vulnerability characteristics
3. Associations of circulating MCP-1 levels with the status of the plaque in terms of presence of symptoms (symptomatic vs. asymptomatic)
4. Associations of circulating MCP-1 levels with the primary composite endpoint of secondary cardiovascular events.

```{r MCP1 Serum Correlations}

# Installation of ggcorrplot()
# --------------------------------
if(!require(devtools)) 
  install.packages("devtools")
devtools::install_github("kassambara/ggcorrplot")

library(ggcorrplot)

# Creating matrix - natural log transformed
# --------------------------------
AEDB.CEA.temp <- subset(AEDB.CEA, 
                          select = c("MCP1_LN",
                                     TRAITS.BIN, TRAITS.CON, 
                                     "Symptoms.5G", "AsymptSympt", "EP_major", "EP_composite")
                                    )

AEDB.CEA.temp$CalcificationPlaque <- as.numeric(AEDB.CEA.temp$CalcificationPlaque)
AEDB.CEA.temp$CollagenPlaque <- as.numeric(AEDB.CEA.temp$CollagenPlaque)
AEDB.CEA.temp$Fat10Perc <- as.numeric(AEDB.CEA.temp$Fat10Perc)
AEDB.CEA.temp$IPH <- as.numeric(AEDB.CEA.temp$IPH)
AEDB.CEA.temp$Symptoms.5G <- as.numeric(AEDB.CEA.temp$Symptoms.5G)
AEDB.CEA.temp$AsymptSympt <- as.numeric(AEDB.CEA.temp$AsymptSympt)
AEDB.CEA.temp$EP_major <- as.numeric(AEDB.CEA.temp$EP_major)
AEDB.CEA.temp$EP_composite <- as.numeric(AEDB.CEA.temp$EP_composite)
# str(AEDB.CEA.temp)
AEDB.CEA.matrix.serum <- as.matrix(AEDB.CEA.temp)
rm(AEDB.CEA.temp)

corr_biomarkers_serum <- round(cor(AEDB.CEA.matrix.serum, 
                             use = "pairwise.complete.obs", #the correlation or covariance between each pair of variables is computed using all complete pairs of observations on those variables
                             method = "spearman"), 3)
# corr_biomarkers

corr_biomarkers_serum_p <- ggcorrplot::cor_pmat(AEDB.CEA.matrix.serum, use = "pairwise.complete.obs", method = "spearman")

# Add correlation coefficients
# --------------------------------
# argument lab = TRUE
ggcorrplot(corr_biomarkers_serum, 
           method = "square", 
           type = "lower",
           title = "Cross biomarker correlations", 
           show.legend = TRUE, legend.title = bquote("Spearman's"~italic(rho)),
           ggtheme = ggplot2::theme_minimal, outline.color = "#FFFFFF",
           show.diag = TRUE,
           hc.order = FALSE, 
           lab = FALSE,
           digits = 3,
           # p.mat = corr_biomarkers_serum_p, sig.level = 0.05,
           colors = c("#1290D9", "#FFFFFF", "#E55738"))

# Creating matrix - inverse-rank transformation
# --------------------------------
AEDB.CEA.temp <- subset(AEDB.CEA, 
                          select = c("MCP1_rank", 
                                     TRAITS.BIN, TRAITS.CON.RANK, 
                                     "Symptoms.5G", "AsymptSympt", "EP_major", "EP_composite")
                                    )

AEDB.CEA.temp$CalcificationPlaque <- as.numeric(AEDB.CEA.temp$CalcificationPlaque)
AEDB.CEA.temp$CollagenPlaque <- as.numeric(AEDB.CEA.temp$CollagenPlaque)
AEDB.CEA.temp$Fat10Perc <- as.numeric(AEDB.CEA.temp$Fat10Perc)
AEDB.CEA.temp$IPH <- as.numeric(AEDB.CEA.temp$IPH)
AEDB.CEA.temp$Symptoms.5G <- as.numeric(AEDB.CEA.temp$Symptoms.5G)
AEDB.CEA.temp$AsymptSympt <- as.numeric(AEDB.CEA.temp$AsymptSympt)
AEDB.CEA.temp$EP_major <- as.numeric(AEDB.CEA.temp$EP_major)
AEDB.CEA.temp$EP_composite <- as.numeric(AEDB.CEA.temp$EP_composite)
# str(AEDB.CEA.temp)
AEDB.CEA.matrix.serum.RANK <- as.matrix(AEDB.CEA.temp)
rm(AEDB.CEA.temp)

corr_biomarkers_serum.rank <- round(cor(AEDB.CEA.matrix.serum.RANK, 
                             use = "pairwise.complete.obs", #the correlation or covariance between each pair of variables is computed using all complete pairs of observations on those variables
                             method = "spearman"), 3)
# corr_biomarkers.rank

corr_biomarkers_serum_p.rank <- ggcorrplot::cor_pmat(AEDB.CEA.matrix.serum.RANK, use = "pairwise.complete.obs", method = "spearman")

# Add correlation coefficients
# --------------------------------
# argument lab = TRUE
ggcorrplot(corr_biomarkers_serum.rank, 
           method = "square", 
           type = "lower",
           title = "Cross biomarker correlations", 
           show.legend = TRUE, legend.title = bquote("Spearman's"~italic(rho)),
           ggtheme = ggplot2::theme_minimal, outline.color = "#FFFFFF",
           show.diag = TRUE,
           hc.order = FALSE, 
           lab = FALSE,
           digits = 3,
           # p.mat = corr_biomarkers_serum_p.rank, sig.level = 0.05,
           colors = c("#1290D9", "#FFFFFF", "#E55738"))


# flattenCorrMatrix
# --------------------------------
# cormat : matrix of the correlation coefficients
# pmat : matrix of the correlation p-values
flattenCorrMatrix <- function(cormat, pmat) {
  ut <- upper.tri(cormat)
  data.frame(
    biomarker_row = rownames(cormat)[row(cormat)[ut]],
    biomarker_column = rownames(cormat)[col(cormat)[ut]],
    spearman_cor  =(cormat)[ut],
    pval = pmat[ut]
    )
}

corr_biomarkers_serum.df <- as.data.table(flattenCorrMatrix(corr_biomarkers_serum, corr_biomarkers_serum_p))
DT::datatable(corr_biomarkers_serum.df)

corr_biomarkers_serum.rank.df <- as.data.table(flattenCorrMatrix(corr_biomarkers_serum.rank, corr_biomarkers_serum_p.rank))
DT::datatable(corr_biomarkers_serum.rank.df)

# chart of a correlation matrix
# --------------------------------
# Alternative solution https://www.r-graph-gallery.com/199-correlation-matrix-with-ggally.html
install.packages.auto("PerformanceAnalytics")
chart.Correlation.new <- function (R, histogram = TRUE, method = c("pearson", "kendall", 
    "spearman"), ...) 
{
    x = checkData(R, method = "matrix")
    if (missing(method)) 
        method = method[1]
    cormeth <- method
    panel.cor <- function(x, y, digits = 2, prefix = "", use = "pairwise.complete.obs", 
        method = cormeth, cex.cor, ...) {
        usr <- par("usr")
        on.exit(par(usr))
        par(usr = c(0, 1, 0, 1))
        r <- cor(x, y, use = use, method = method)
        txt <- format(c(r, 0.123456789), digits = digits)[1]
        txt <- paste(prefix, txt, sep = "")
        if (missing(cex.cor)) 
            cex <- 0.8/strwidth(txt)
        test <- cor.test(as.numeric(x), as.numeric(y), method = method)
        Signif <- symnum(test$p.value, corr = FALSE, na = FALSE, 
            cutpoints = c(0, 0.001, 0.01, 0.05, 0.1, 1), symbols = c("***", 
                "**", "*", ".", " "))
        text(0.5, 0.5, txt, cex = cex * (abs(r) + 0.3)/1.3)
        text(0.8, 0.8, Signif, cex = cex, col = 2)
    }
    f <- function(t) {
        dnorm(t, mean = mean(x), sd = sd.xts(x))
    }
    dotargs <- list(...)
    dotargs$method <- NULL
    rm(method)
    hist.panel = function(x, ... = NULL) {
        par(new = TRUE)
        hist(x, col = "#1290D9", probability = TRUE, axes = FALSE, 
        # hist(x, col = "light gray", probability = TRUE, axes = FALSE, 
            main = "", breaks = "FD")
        lines(density(x, na.rm = TRUE), col = "#E55738", lwd = 1)
        rug(x)
    }
    if (histogram) 
        pairs(x, gap = 0, lower.panel = panel.smooth, upper.panel = panel.cor, 
            diag.panel = hist.panel, ...)
    else pairs(x, gap = 0, lower.panel = panel.smooth, upper.panel = panel.cor, ...)
}

chart.Correlation.new(AEDB.CEA.matrix.serum, method = "spearman", histogram = TRUE, pch = 3)

chart.Correlation.new(AEDB.CEA.matrix.serum.RANK, method = "spearman", histogram = TRUE, pch = 3)


# alternative chart of a correlation matrix
# --------------------------------
# Alternative solution https://www.r-graph-gallery.com/199-correlation-matrix-with-ggally.html
install.packages.auto("GGally")

# Quick display of two cabapilities of GGally, to assess the distribution and correlation of variables 
library(GGally)
 
# From the help page:
ggpairs(AEDB.CEA,
        columns = c("MCP1_rank", TRAITS.BIN, TRAITS.CON.RANK, "Symptoms.5G", "AsymptSympt", "EP_major", "EP_composite"), 
        columnLabels = c("MCP1 (serum)", 
                         "Calcification", "Collagen", "Fat 10%", "IPH", "Macrophages", "SMC", "Vessel density",
                         "Symptoms", "Symptoms (grouped)", "MACE", "Composite"),
        method = c("spearman"),
        # ggplot2::aes(colour = Gender),
        progress = FALSE) 
```


# Session information

------

    Version:      v1.0.5
    Last update:  2020-03-05
    Written by:   Sander W. van der Laan (s.w.vanderlaan-2[at]umcutrecht.nl).
    Description:  Script to select samples from the Ather-Express Biobank Study.
    Minimum requirements: R version 3.5.2 (2018-12-20) -- 'Eggshell Igloo', macOS Mojave (10.14.2).

------

```{r eval = TRUE}
sessionInfo()
```

# Saving environment
```{r Saving}
save.image(paste0(PROJECT_loc, "/",Today,".",PROJECTNAME,".sample_selection.RData"))
```

------
<sup>&copy; 1979-2020 Sander W. van der Laan | s.w.vanderlaan-2[at]umcutrecht.nl | [swvanderlaan.github.io](https://swvanderlaan.github.io).</sup>
------

